Justin Caldbeck

From Our Valued VC: Lightspeed Venture Partner’s Justin Caldbeck

We are so fortunate to have such great backers at BloomReach – from Bain Capital Ventures, to NEA, to Lightspeed Venture Partners. As some of you may have seen, Bain Capital Ventures’ Ajay Agarwal did a great job representing us on Bloomberg TV on launch day!

In addition, Justin Caldbeck from Lightspeed Venture Partners posted a wonderful blog on the Lightspeed Venture Partners’ blog. We encourage you to read his article in full but wanted to highlight some awesome comments for our audience as well.

“Congrats to Raj, Ashutosh and the rest of the Bloomreach team on their exciting next chapter and for developing a truly an amazing Mobile product.  This will help the nearly 100 large retailers that they work with today and many, many more drive a highly relevant experience for their customers and a meaningful increase in conversion and revenue. I’m proud to call them both friends, to have been there from the very beginning and to continue supporting them in their continued movement toward being the preeminent big-data driven marketing company for e-commerce and mobile commerce.”

ResponsiveDesign_03

Why Mobile Shoppers Don’t Respond to Responsive Design

“We’re seeing more traffic from mobile devices, and we’ve built a stellar mobile site, but why aren’t our customers growing?” This is a more-than-common question asked by CMOs in countless marketing meetings every day. Recently, I explored that topic in an article for WIRED.

By the third quarter of last year, the global smartphone population topped one billion – putting it at a higher growth rate than humans. Yes, more smartphones are activated each day than babies are born. Who would have thought that in less than a decade, a plague seemingly more contagious than any other would be a little glass box that is truly an extension of a unique person? Disagree? Whether you believe Mary Meeker’s statistic about people checking their phones 150 times a day – believe this – think about the separation anxiety you feel when you can’t find your “phone.” You can admit it; you keep a keen eye on any person who asks to see your phone.

“You want my passcode? Why?”

There is no device more personal and consistently accessible than a smartphone, and retailers know it.  According to eMarketer, U.S. mobile commerce sales jumped 81 percent in 2012; and by 2016, mobile commerce will make up 24 percent of all retail ecommerce. However, you can’t just shrink your website and expect new or even loyal customers to fill their carts with your goods. So, to accommodate that growth and create better experiences, brands worldwide have turned to principles of responsive design to shrink entire stores – from shelves to checkout – to handheld touch screens  Every millimeter of real estate on a screen has been optimized to display content in a visually appealing manner that is conducive to easier paths to purchases.

For many sites, responsive design makes a lot of sense. It makes managing the site far simpler for the marketing, merchandise, ecommerce and IT teams. Essentially, they only need to update one place for the change to push to each and every platform. You want that content optimized automatically for the device you are on, but that’s the only response the site makes to suit your needs.

In the end, well-executed responsive mobile website design results in a beautiful site for clumsy thumbs and easier execution of perfunctory tasks. Ironically though, successfully deployed mobile sites with great responsive design are only the beginning, not the end. What if you have a beautiful mobile site where no one can find what they want? Beautiful content is useless content if it is irrelevant. Consumers have little patience on smartphones – they are accessing them for seconds while getting on a bus, a minute or two while waiting in line. Google found that 61 percent of consumers on mobile devices will quickly move on if they don’t find what they are looking for right away.

Thus, in order to effectively compete in mobile commerce, mobile retailers need to start thinking about providing responsive experiences. Responsive experiences are driven by predictive, data-driven insights that create unique experiences for mobile shoppers. They know the profile of a visitor by combining all of the insights from any past visit, location-based data, activity of like users and traffic origin. With every action or inaction, the engine behind the site should learn and continually refine to provide contextual suggestions and outcomes that have a high probability of being correct.

Mobile users accessing content on limited space while having low tolerances are much more likely to hit the back button faster if they have to manually refine an experience. Think about what makes in-store shopping experiences so superior to web experiences. There are sales representatives who use intuition to aid your search or remember your past visits, there are complementary products showcased in the right areas and the store layout is conducive to browsing. Why shouldn’t mobile sites emulate this experience and be as much like a mobile sales rep as possible?

And speaking of in-store experiences, research shows that more than 70 percent of consumers use their smartphones while shopping in a store, and the majority who use their smartphone actually visit that store’s own mobile site. They are looking for product breadth, size, color and price parity. Only a responsive experience on a smartphone — unlike a desktop or tablet — can adapt while consumer is physically present in a store. Imagine that you are in your favorite retail store comparison shopping, and the store is able to provide you with specials based off of your browsing history or lists you may have created through proactive alerts.

The challenge of creating relevant experiences for users is a daunting one. The average business has a lot of products and many different ways for visitors to find them, including many channels representing thousands of possible searches, campaigns, ads, emails, social media, etc. And while marketers and merchants think about channels, cross-channel alignment and omni-channel experiences – the consumer thinks about their goals – shopping, saving time and enjoyment.

So, what are some of the cues that indicate that a mobile site needs responsive experiences?

  • Is time-on-site significantly lower on the mobile site than Web?
  • Is the abandon rate significantly higher for mobile sites than the Web?
  • Are site-search users on a mobile site converting?
  • What is the repeat usage of the search functionality?
  • Is mobile traffic increasing while revenue-per-visit fails to keep up?
  • Do click paths on mobile sites indicate that users are lost or frustrated – e.g. bouncing from category page to product page back to category page?
  • Would using your mobile site in your stores help the shopper in any way?
  • Are you “learning” from user behavior – either with your team or with machine-learning?
  • Beyond learning, are you taking action?

The bottom line: Just changing the look and feel of a conventional website is not enough to meet the special needs of a mobile user. It is vital to be intent-aware in determining the content delivered to a mobile user because of the differences in their intent, ergonomics and size of the device, and even inventory in nearby stores. Sites like Amazon — which owns an astonishing 59.4 percent of mobile department store visits — are already throwing billions at getting to know individual consumers in unique ways — are you?

 

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Not all searchers are the same: why savvy marketers separate branded vs. non-branded traffic

If you’re Nikon, you may instinctively believe that the online shopper behind the query “Nikon Coolpix” is more valuable to you than the one who types “compact digital camera with 10x digital zoom,” because of the former shopper’s likelihood to convert.  Yet, converting the person who types in “compact digital camera” may prove more valuable over the long run, from both cost of customer acquisition and lifetime value perspectives.

Savvy marketers differentiate between branded and non-branded search queries as they set up, track and evaluate their search marketing efforts.  In this post, I’ll share why marketers should make this distinction to best evaluate effective revenue share for each channel.

First, here’s what we mean when we say branded or non-branded traffic.  Branded traffic contains your site’s brand name.  If you’re Williams-Sonoma, that includes “Williams-Sonoma,” “williams-sonoma.com” and “Williams-Sonoma essential oils.”  Not surprisingly, branded traffic often originates from returning customers that tend to have higher conversion rates.  These searchers know your brand and are looking for you; other marketing channels tend to heighten their awareness of you.

Non-branded traffic does not contain your site’s brand name and is comprised of a higher percentage of new customers.  Even previous customers who weren’t looking for your brand when searching represent potential incremental revenue.  If these customers have a good experience with your product, they’re more likely to purchase in the future, thereby representing a good opportunity to build lifetime customer value.  And if these customers don’t purchase from you, but rather from a competitor, the competitor gains the incremental revenue, new customer acquisition, and potential future branded traffic and purchases.

As you can see, branded vs. non-branded traffic comprises two different customer segments: one who knows you and is likely to purchase and one who is likely to be a new customer that will have a bigger lifetime value if you convert them.  Separating branded vs. non-branded traffic when managing and evaluating search traffic is critical for a number of key reasons:

  • There is a cap to the number of branded searches that can be made.  This tends to be driven by advertising, content or prior experiences. Growing brand awareness, brand demand, and non-branded search traffic are ways to expand the total number of branded searches.
  • There is no cap on non-branded search terms; in fact, this is infinite.  The only boundaries on non-branded search terms are the collection of products you offer and your ability to describe those products in ways that are relevant to the largest population of customers.
  • The economics of these two types of traffic are different.  Branded terms tend to be cost-effective and convert well.  Consolidating them with non-branded terms will skew results and muddy optimization.
Branded Search Non-Branded Search
Visits 1000 1000
CPC $0.07 $0.35
Cost $70 $350
Conversion Rate 3% 1%
AOV $100 $100
Revenue $3000 $1000
Effective Rev Share 2.3% 35%

Because they represent different customer groups with different economics, it’s critical for online marketers to differentiate between branded and non-branded search queries as they set up, track and evaluate their search marketing efforts.  In future posts, I’ll discuss a few ways that you can do this, such as how to calculate the value of each type of customer.

 

red-suede-shoes-example

Explaining the Uniqueness Score in CQM

When you land on a category page, you want a good selection of choices that fit exactly the intent (or keyword search) that brought you to that page. You are looking for what you want specifically, and you’d like the retailer to present a unique page that is organized with their best, most relevant products for you to choose.

Delivering a unique experience is not a new concept to marketers. SEO professionals know that search engines value unique content because it provides better user experiences by guiding visitors to more unique and closer-matching landing pages. However, many e-commerce teams don’t know that overlapping pages too closely also creates friction in funneling users to the best possible pages, resulting in bounces instead of conversions. To help marketers better provide unique experiences while tracking overlapping content, the BloomReach Continuous Quality Management (CQM) technology quantifies this content overlap through a Uniqueness Score.

The Uniqueness Score measures how unique the content and set of products are compared to the closest duplicative page on a site. A higher Uniqueness Score means that the overlap of content and products is minimal compared to the next closest page.

For example, consider two pages: “Red Suede Shoes” and “Blue Suede Shoes.” The “Red Suede Shoes” page has 12 products, 10 of which also appear on the “Red Leather Shoes” page. Conversely, the “Blue Suede Shoes” page has seven products, only one of which appears on another page, “Blue Leather Shoes.” In this case, the “Blue Suede Shoes” page provides a more unique experience and receives a higher Uniqueness Score.

Naturally, there will be some product overlap between pages, which is fine. Not everyone searches in the same way, so you need to create clusters of pages with somewhat related content to better match user intent. While building out these pages, the Uniqueness Score can help make sure that the pages you do create are differentiated. A healthy balance of unique pages that are topically related to one another is key to providing customers with a quality site experience.

stats-flux

Looking at the Flux Scores in CQM

There’s a temporal component to a quality web page. Marketers, visitors and search engines appreciate a page that is consistently “good,” meaning the content is what they expect, and there are enough different products to satisfy their desire for choice. So, if a page provides a high-quality experience one day and then a poor experience the next, there could be a problem.

The Flux Scores in BloomReach’s Continuous Quality Management (CQM) technology help marketers measure and act on these fluctuations. Flux Scores reflect how often the content on a particular page is changing. If the content on a page changes too frequently, the page may deliver an unpredictable experience to visitors.

Let me give you an example. Say you have a “Dark Wooden Desk” page that has 10 products on the page today. Tomorrow, six of the desks go out of stock leaving only four left on the page. Two days later, 10 products are added to bring the total products to 14. One week later, 12 products are removed from the page because they are discontinued, leaving only two products.

With so much variability, a page like this can drive an online marketer crazy because it will have performance implications: conversions and revenue will be up one day and down the next as the number of products and the amount of content rises and falls. Also, search engines appreciate content consistency, so pages that fluctuate too frequently may experience an impact to organic search traffic.

No matter what comprises your product catalog, you want a consistent, high-quality experience for customers and search engines. Flux Scores can help you understand where opportunities exist to create more consistent content.

amazon-logo-2013

Have You Recognized Amazon as the Top Competitor?

Amazon is winning and competing with them is hard, but every ecommerce company must have a strategy to address the Amazon juggernaut. With their incredible product breadth, strong customer service and continuous investment in their user experience, they are continuing to attract more and more customers. In the U.S., their non-media revenue grew 28 percent year on year.  And, they are investing a greater percentage of their revenue in technology and content (7.9 percent up from 6.5 percent) to deliver the best service and the most relevant experience to every visitor.

As consumers rapidly shifts to mobile on smartphones, Amazon is capturing even more of the market with 59.36 percent of mobile department store visits – that’s 14 percent more than their incredible share on the web.  In addition, it translates to $1.2 billion of investment, not including their acquisitions.

Consumers are taking notice, too.  According to Forbes:

  • Forrester Research found that a third of online users started their product searches on Amazon compared to 13 percent who started their search from a traditional search site; and
  • comScore found that product searches on Amazon have grown 73 percent over the last year while shopping searches on Google have been flat.

Leaders in eCommerce work hard to attract consumers to their sites – using email, social media, paid search, natural search, cross-channel initiatives and – where possible – their physical stores. When they succeed in attracting a new consumer, their sites aren’t built to deliver the most relevant experience and consumers bounce. If the consumer isn’t presented with something relevant or if it is hard to discover something they want through the experience, it’s not like they forego making the purchase.  Instead, they go to Amazon.com, and it’s important to note that the consumer’s perseverance in the mobile world is even less than the traditional web.

Every ecommerce executive I meet will talk about the urgency of competing with Amazon.com and building cross-channel initiatives to leverage assets that Amazon.com doesn’t have (stores), but the iteration process hasn’t caught up. Too many ecommerce companies are stuck in four to six to 12-month iteration cycles where there are more meetings than actual development.  Amazon.com is running 200 tests a day. To compete, you must be configured to iterate, and you must have data that closely tracks what happens when you experiment.

To compete with Amazon.com, it’s important to keep two things in mind:

  1. The entire experience must be relevant – from the campaign through the sale conversion. It must be a quality experience – where the content matches the consumer’s intent. Clearly, if there are good choices for the consumer, then it’s easy for them to convert.
  2. Business in ecommerce must tap into data beyond their site and CRM – because Amazon.com has access to web-wide data and effectively uses it.
  3. Most importantly, IT, ecommerce and marketing need to align priorities around rapid iteration and innovation – focusing on creating the highest-quality, differentiated user experience.

 

Understanding the Multi-Channel Shopper

Search marketers need to understand that a growing number of consumers are becoming multi-channel animals. The path to purchase isn’t a straight shot – it’s a disjointed, windy route through reasonably uncharted terrain. But, because this area is so new, it’s hard to understand the different profiles that you need to engage. Recently, I described five profiles of multi-channel shoppers for Search Engine Watch, so I wanted to share my thoughts on our blog, as mobile commerce will continue to rise.

Consumers are making more of their purchasing decisions on mobile devices – whether on a smartphone or tablet – in addition to making it one of their primary channels to research products.

A recent survey by Telemetrics and xAd showed that 50 percent used their mobile devices to start the discovery process and 46 percent used mobile exclusively when performing research online. Even Google noted last year that 65 percent of online searches began on a smartphone.

This emerging multi-screen reality has led to CMOs worldwide to begin their own research – how do we capture and convert multi-channel shoppers? Good question.

The mobile shopper can act much differently than one browsing off of a PC. They’re at restaurants, on buses, or even in stores – often with only seconds to check out (and not necessarily “checkout”) a product out before moving on to the next task.

Tolerance for irrelevant content and slow/bad site-search functionality is much lower for mobile shoppers. Couple that with the fact that they move between devices, and you have a recipe for significant losses because even one bad experience at one of the points of discovery or conversion can lead lost customers.

However, before you even begin to execute any strategy of customer segmentation, you have to understand the different profiles of multi-channel shoppers and consider the right tactics to monetize each profile.

What follows are five distinct profiles of multi-channel shoppers, as well as some methods to convert them to customers. Please note that each profile isn’t mutually exclusive – many shoppers can exhibit behavior of multiple profiles, which is why gathering data across devices, channels and sessions is imperative to predicting what will be the most relevant content to present at this moment.

1. Need-It-Now Profile

This shopper has discovered the specific product they want and is ready to make a purchase; however, they’ll almost certainly want to find a physical retailer that has that product in stock or that can get it shipped very quickly, like Amazon’s same-day delivery service.

Having fresh web pages with the most up-to-date information is imperative.

For example – if a consumer is presented with a mobile search result for a “red strapless dress” that is on sale for $50, you better have location-specific data that this is, in fact, true. This means that your inventory information should be as close to real-time as possible.

Also, these users have very specific long-tail searches, making predictive-search capabilities extremely important. If available, know their browsing history and ultimately have their query available in a drop-down menu within a few taps. Typically, this is the profile of someone who shops on Amazon.

2. Bargain-Hunter Profile

This shopper uses their smartphone to compare prices either before or while they are in your store. Commonly referred to as “showroomers,” they are extremely price-sensitive and have very little brand loyalty.

It’s important to offer as much incentive information as possible. So, they should be provided with any warranty information or in-store special promotions.

Anonymously identifying and matching IP addresses of store Wi-Fi to see if they are in a store while accessing your mobile site is a good way to track these types of shoppers. Capture the data and track the behavior of other shoppers who visited your site while in store to gain invaluable insight.

3. Right-For-Me Profile

These shoppers visit your site at home on a desktop or laptop, on their smartphones and while in-store, and express very specific and consistent intent signals. They often search for and purchase certain sizes or brands/designers at specific price points.

It’s important to piece together individual experiences and present them with the most relevant content based off of their habits. You can compare the behavior of your authenticated users on mobile and web to learn about their preferences, and then try to apply it to their content.

Offering “More like this” widgets that take into account all of the previous expressions of intent can help keep that customer converting.

4. Time-to-Kill Profile

These shoppers are most likely your exploratory buyers, with a little extra time, where easy navigation and visual design elements that create a “fun” experience are important.

Using social-network data from Pinterest or Facebook, consider creating landing pages of popular or emerging products that can turn a browser into a customer. They’re going to explore more pages and provide you with a lot more data about what products could be linked.

By tracking bounce rates and time-on-page metrics, you can learn what products make better sense to present together.

5. Most-Valued Customer

These shoppers are the ones that engage with you the most across multiple channels or devices. They click through emails and discover a lot of your content, and should be the most important.

These customers shouldn’t be treated like any other customer – you should understand their intent and present them with the right offers at the right time. What time of year do they normally buy gifts, are they tied to a holiday, and are they only selecting sale items? While this may sound like something very simple, actually tracking this data at a granular level to scale for potentially thousands of people while knowing which product is right based on their previous history across devices – all accurately – is a tremendous task.

Summary

Understanding, processing and acting on all of this available data is a problem well beyond the scope of humans – it’s an issue for big data science. Amazon is doing a great job to capture the shoppers frustrated with experiences on other retailer’s mobile sites.

As consumers rapidly shift to mobile shopping on smartphones, Amazon is capturing an unbelievable 59.36 percent of mobile department store visits. And, they are investing a greater percentage of their revenue in technology and content – 7.9 percent up from 6.5 percent – because they realize that high-quality content that is relevant to each shopper is a problem that only a machine can address at scale.

In the last decade, search marketers have been faced with a gargantuan amount of online data and web analytics, and technologies that have helped process the information have barely allowed them to keep pace. Mobile data – and the different channels that it opens up – ups the ante exponentially. At the same time, consumers expect a seamless and relevant experience no matter their platform of choice.

The most successful companies will embrace a multi-channel initiative. Those that don’t will only face a bigger digital divide between themselves and consumers – ultimately losing them to the likes of Amazon.

Marketing With IT; Not Marketing Versus IT

Whether you’re in the mailroom or the boardroom; the front lines or behind the curtains, technology advancements have caused unnecessary rifts within organizations. The one thing that hasn’t changed about businesses is that every employee should be in it for the good of the organization. Recently, I shared my thoughts in a leading marketing publication iMedia Connection on how marketing can work together with IT to help companies keep pace with the growth of technology.

I’m sure many of us can remember a time when marketing and information technology could carry on for years in the same organization and never even see each other. Sure, they may have shared a “C” position in the boardroom, but the two might as well have spoken different languages. Subsequently came the rise of the Internet – the need to tract, analyze and adapt to consumers that existed as a collection of “1s” and “0s,” but the hardware and software to act on this data was rudimentary and expensive. Then, the limitations of housing the data virtually erased with the advent of the cloud and other forms of cheaper storage – which blew off the lid of marketing intelligence. The CMO became voracious for data as technology became more sophisticated. Who would have thought that in a few short years, the CMO could outspend CIO for technology?

This is the technology territorial struggle taking place within organizations, which is also gaining more and more attention in both fields. But, which area has the responsibility for the growing demand for technology in marketing? While big data has not traditionally been part of the CMO’s role, CMOs will either have to exert more authority or talk in terms that are important to CIOs.

The CMO is quickly becoming the major data consumer in business, responsible for gathering insights from all the unstructured data available. CMOs also are becoming big users and buyers of technology, which makes them more strategic. Marketing is accustomed to quick experiments, whereas IT thinks in terms of 18-month-long projects. Marketing is often the pebble in IT’s shoe, constantly nagging for one-off projects, quick fixes or data analysis.

Conversely, marketers don’t necessarily factor in the total cost of determining return on investment, nor do they worry about the impact on performance, reliability or security. CIOs might evaluate the platform based on cost savings, productivity increases and impact to the overall infrastructure, whereas marketers look at the marginal revenue gained for bringing in new customers. The central challenge is balancing these two valid, but often disparate objectives in a way that minimizes total cost of ownership and maximizes ROI.

Clearly, there is an evolution taking place, and CMOs can work in partnership with their fellow C-level executives for a win-win. I believe the most successful companies will use technology and expertise to carve out ways to act in two-week increments, implementing large-scale projects that move at the speed of marketing with the reliability and performance of IT. We are already seeing the rise of hybrid roles – a marketing technologist as some have called it (a lot of companies even have their own “Chief Marketing Technologist”). On the other side, data scientists specifically dedicated to handling terabytes of data. Now more than ever before companies are tracking consumers every move – combining intelligence from brick-and-mortar locations to interactions on the web. The emergence of cross- or multi-channel initiatives is quickly becoming a box that both CMOs and CIOs must check for their boss – the CEO. And it should be. Consumers are coming from the Web, the street, social media, mobile devices, emails and soon-to-be-huge voice controls, so the amount of available data is only going to grow exponentially.

Most marketers are ready to pounce on every platform, but by opening up these doors, you expose yourself to a ton of security risks and performance issues that hurt other critical functions of the enterprise. It is the function of IT to scrutinize every aspect of technology integrations accounting for all threats – things that take time which marketing never seems to have. However, there are ways to bridge this gap and both work toward the ultimate common goal – growing a business (and keeping a job!). Here are a couple of successful approaches for marketing when approaching the situation.

Successful Approach 1:

Bring IT into the conversation early after determining that major data-driven initiatives or strategies are needed to effectively compete. When marketing starts to explore technologies to help scale their campaigns, inviting someone from the IT team to join the evaluation and ask their questions early will save everyone time, identify issues marketing might not consider and align everyone towards success. Sending requests “over the transom” breeds frustration and misunderstanding. Identifying key integration concerns is not a conversation for the boardroom – it should start when evaluating different vendors or specific technologies. You should question any marketing technology vendor that claims to be “plug-and-play” yet bring serious revenue generation.

Successful Approach 2:

Consider dedicating or hiring marketing technologists to the marketing team who can liaise or even come from IT. These people are responsible for assessing the technology ramifications of a marketing solution, estimating the implementation issues and often can handle the implementation itself. Face it – most of the time, the language is inherently different, and our world is changing at a pace that only the winners will keep up with. You may remember that there was a time when a “CIO” was a not-so-common position.

Successful Approach 3:

CMOs and CIOs should have a standing meeting to discuss marketing-driven initiatives and understand IT initiatives. Progressive CIOs are allocating time and teams for rapid response to business needs so that the teams on long-term initiatives – re-platforming, new applications – and are not distracted by the repeated requests of marketing.

The bottom line is that successful organizations are adopting better communication and cooperation channels within the organization – not just those at the cutting-edge of technology adoption. This “data thing” isn’t going away, and even SMBs are stepping up their game in big data. As the recent technological disruptions have demonstrated, no one is safe and your competitors are almost assuredly evolving, so are you?

BloomReach Adds ShopLogic Team to Expand Big Data Marketing Applications

When we founded BloomReach more than four years ago, our vision was to create an online experience that was excellent for consumers by providing relevant content, while allowing retailers to get more of their products discovered on the increasingly noisy web. This grand vision doesn’t stop at search, but extends across all channels, especially as the avenues from which consumers come keep increasing.

However, bridging the digital divide between consumers and online retailers is an extremely difficult, data-intensive problem that requires the best talent with broad skillsets from all fields of ecommerce. Today, I am proud to announce that BloomReach has brought on the co-founders behind ShopLogic, the promotions management platform company. When we met CEO Kevin Chan and CTO Dennis Maskevich a little while ago, we recognized the talent and industry knowledge that the two offered and began discussions to bring their expertise to help us meet BloomReach’s significant growth. Kevin and Dennis represent the highest caliber of dedication and drive, and we all decided that the best way to fully realize our shared vision for online commerce was to build upon BloomReach’s technology with them as part of the BloomReach team.

Since launching in 2011, ShopLogic provided an intelligent way to manage and optimize promotional marketing using customer interaction and purchase data. The company was highly successful with many mid-sized B2B customers and helped them to achieve an 11 percent increase in net revenue. Driving greater return for e-commerce marketers through better visibility and profitability is quite a feat with the digital marketplace being as competitive as it is, and we are elated to have Kevin and Dennis onboard as we plan to have a breakout year!

ShopLogic originally launched with seed funding from big data VC firm Data Collective and AngelPad, a mentorship program started by ex-Googler Thomas Korte. Before founding ShopLogic, both Chan and Maskevich worked at Adchemy, a prominent online advertising technology company. ShopLogic’s customers are still able to use its platform, but the technology will operate independently from BloomReach’s platform.

The technology and quality of BloomReach is measured by the excellence of its team, and along with everyone at BloomReach, I encourage you to stay tuned in the coming months for many more exciting announcements.

CQM – Understanding the ‘Content’ Score

“Content is key” could be the most-used, yet least-measured saying in the online world. Ok, so you need content, and it should be good content if you want visitors to engage, subscribe, buy or otherwise convert. But how do you quantify the quality of your content? We’ve looked at this challenge and developed “Content Scores” as part of our CQM technology.

Judging content quality on an editorial, video or photo-sharing site likely revolves around views. But with ecommerce, it’s not that simple. Put yourself in the shopper’s shoes, and you’ll see what I mean. To a shopper, quality content means the page contains what they are looking for, and they have some options in order to find that perfect pair of jeans, non-stick grill pan or bridal-shower gift. You expect the page you land on to wow you with great choices.

So, if we start with a consumer’s high expectations and work backwards into what we would need to measure to ensure the content is high quality, we end up focusing on two things:

1) The relevance of the products to the page theme – basically, how well do the products on the page align to that page? For example, imagine 2 pages with the following titles: Green Lace Dress and Little Black Dress. The Green Lace Dress page has 10 products, 8 of which contain the words “green lace dress” in their titles and descriptions. Little Black Dress has 4 products on the page, and only 1 product contains “little black dress” in the title and description. In this case, the Green Lace Dress page is a better user experience and has better content scores because it has deeper, more relevant content vs. the Little Black Dress page.

2) The number of products on the page. Are there enough products matching the theme to give the shopper the selection they would like? That shopper looking for “minimalist running shoes” is expressing intent to research a purchase, hence the somewhat broad search. If the page only has 1 or 2 pairs, that’s unlikely to satisfy their curiosity as to their shoe options.

High Content Scores will indicate that a page is optimized to match the shopper’s desire for a selection of choices that all suit their needs. After all, if you were the shopper, isn’t that what you’d expect from a great site?

Upcoming posts will cover CQM Uniqueness, Flux and Behavior scores.

 

In a Machine World, Humans are the Key

It’s easy to understand the hype, hysteria and confusion in the technology community right now – big data and the technology needed to understand, process and monetize it are essential to compete in today’s marketplace. Where would we be without the machines, the computer software, or the seemingly incomprehensible mathematical algorithms that power our digital world?

It’s appears seamless for the most part, but even the simplest parts of our lives are guided by invisible formulaic pathways. A prime example would be this past Mother’s Day, millions of people went to their computers on a mission to find a great gift, and keyed a simple search for a “perfect Mother’s Day gift.” It’s easy to forget that – after you hit “enter” – vast amounts of digital cues were sent out throughout the entire Web simultaneously crawling billions of pages, and thousands of merchandisers automatically analyzed their inventory to match that search with products that satisfied the demand. With a few clicks, the consumer is satisfied, but in the background, a mind-boggling amount of digital algorithmic exchanges occurred to vie for your attention.

We all know that machines accomplish tasks faster, more efficiently, without hesitation or a complaint, but does that mean humans should hand everything over to algorithms that automate everything? Absolutely not! Human creativity and intuition are irreplaceable and should maintain the quality control behind machine-based decisions. As we’ve seen on numerous occasions, machines don’t always get it right; and it takes a combination of the two to collect, analyze and respond in our data-driven world.

I’ll acknowledge that there is no way that humans can compute at the the scale that machines can to evaluate terabytes of data, constantly changing variables and minute calculations required to compete in today’s online market, but machines should serve as an army of support staff that operate as the workhorse of our initiatives – the way they always have been. People have the ultimate “say so” and should ensure that larger actions are executed with the highest quality standard. For its part, machine technology with the ability to learn like our Web Relevance Engine (WRE) – the “brain” behind BloomReach that performs 1,000 Hadoop jobs and crunches terabytes of data – is designed to monitor, figure out and act on an opportunity without the resources required to manually handle the job. Even our system requires a human review component to review and calibrate the suggestions of the big data application and discern the correct strategy.

A recent New York Times article by Steve Lohr explored this idea quite well, explaining that although algorithms are growing ever more powerful, fast and precise, the computers themselves are literal-minded, and context and nuance often elude them. Mr. Lohr is right – while machines can help you determine context and subtle nuances, anything claiming to be automatic has a 100 percent chance of being wrong at some point.

At BloomReach, our mission is to provide technology capable of deeply understanding web-wide consumer demand and intent with a compilation of continuously learning algorithms that help capture the unmet demand with our customers’ relevant products. It would take swarms of data scientists, marketing experts and IT specialists to do what we do at the scale of our engine, but that doesn’t mean that humans aren’t a part of the equation. We combine the collective intelligence of the WRE with members of our team to ensure that the pages we create and the recommendations we make are logical, impactful and meet the highest quality standards available. And even then, our customers are equipped and empowered to make informed decisions.

Just because a plane is capable of landing itself doesn’t mean it should; likewise, ultimately our solution would not be as effective and our company could not have been as successful if humans weren’t sitting at the steering wheel. I’m sure that everyone has heard the saying, “To err is to be human, to forgive is divine.” It’s safe to say that at BloomReach, “To err is to be human and machine, to forget that is unforgiveable.”

BloomReach Introduces Continuous Quality Management Technology

Today, we are pleased to make available to marketers one of the most comprehensive quality management technologies called Continuous Quality Management (CQM). Using our deep understanding of key elements that make up a quality page, CQM introduces four unique classes of scores that allow marketers to manage and control their content. The scores – which include content, behavior, uniqueness and flux – are all powered by BloomReach’s big-data Web Relevance Engine.

At BloomReach, we recognize that to deliver the most relevant experience, machines and humans have to work together. CQM gives marketers more control over the quality of their pages by combining judgment with technology to deliver the best user experience, matching content to intent. BloomReach has always had a keen focus on quality. Now CQM makes our underlying systems available to our customers – ultimately giving them the power to control their content that makes sense for their specific needs.

Matching intent to content is harder today than it has ever been, and more sophisticated digital users expect and require relevance. In addition, consumers express their intent in countless ways and ensuring a high-quality experience, over time, are unmanageable tasks – especially given that 70 percent of all searches are long-tail. CQM presents an understandable way for marketers to take an active role in managing their content without the need to bring in IT – whether through publishing new pages derived from undiscovered expressions of intent, editing pages that aren’t performing well, or retiring pages that are out of date or do not reflect a business’s current objectives.

At BloomReach, we’ve spent the last four years learning and refining our algorithms to create the most relevant and the highest-quality user experience while generating significant lift for our customers in natural search, and now we are making this expertise available to marketers to make their content match their customer’s intent.

Our CQM technology scores generated and suggest pages based on the following:

  • Content is determined by interpreting a page’s topic and comparing the content to the topic.  Content Quality considers the number of unique, relevant products on the page as well and the fit of the products and their attributes to the intent.
  • Behavior is measured by integrating traffic metrics such as bounce rate, page views, time on site, and conversion rate in addition to other factors.
  • Uniqueness is measured using BloomReach’s Dynamic Duplication Reduction (DDR) technology to determine how much of the content on the page is unique versus other pages on the site.
  • Flux captures the rate of change of products on the page, which is critical to understanding why quality can degrade over time and critical to predicting pages that require further inspection.

Also, CQM will be a constantly evolving and improving technology. Stay tuned in the next few months for additional and enhanced functionality.

Innovate For America: BloomReach Supports More H-1B Visas

Today, as Congress debates plans for comprehensive immigration reform, much of the news coverage is focused on the debate about dealing with illegal immigration. But, one issue that is equally important is legal immigration for highly skilled foreign workers facilitated by H-1B visas. As Martin Giles, at The Economist reported today, this is a visa that is used by many high-tech companies, including BloomReach, to hire qualified candidates when they are not available in the U.S.

As you can imagine, one of the most pressing issues facing America today is maintaining an innovative environment where people can do business. Nowhere is this more evident than here in Silicon Valley, and a key component of innovation is hiring the best people. To help highlight the importance of this issue, Scott Sandell from NEA, one of the most prominent venture capital firms in the country and also a BloomReach investor, has started an initiative called Innovate For America.

This initiative is bringing attention to the number of companies founded by immigrants. It’s important to note that increasing the number of H-1B visas actually creates jobs in America for people who are legally authorized to work here. Our country was built on immigration; in fact, some statistics suggest that 40 percent of the Fortune 500 were founded by either immigrants or the children of immigrants.

Along with an informational website created by a team from BloomReach and NEA, the initiative also includes a widget that companies can put on their website to highlight the issue of H-1B visas. Specifically, we want Congress to increase the yearly allotment of visas, which is currently capped at 65,000 and was exceeded in just a few days this year. I encourage you to check out the Innovate For America website and sign up to get the widget on your own company’s website by locating the “get widget.”

Hiring great people is key to sustained innovation, economic growth and job creation. Please join us in supporting Innovate For America.

BloomReach Is The Best Place to Work!

“The very essence of leadership is that you have to have a vision.” – Theodore Hesburgh

BloomReach Best Places to Work 2013

Since founding the company four years ago, Raj De Datta and Ashutosh Garg have built a quality company that not only creates visionary technology, but also cultivates an inspiring atmosphere for its people. The engine that drives BloomReach isn’t just the software architecture or the algorithms. It’s the ability of our company to see itself years into the future, provide a viable roadmap to attain that vision and communicate it to everyone – something that many technology companies lack.

For their part, Raj and Ashutosh have lived, breathed and articulated their vision superbly. I can recall a few representative anecdotes.

The first one goes all the way back to April 2009. I had interviewed at BloomReach and had a job offer. At the time, I was an engineer at Facebook, a company that was growing at a staggering pace and everyone viewed it as the biggest technology company since Google. BloomReach had a one-room shared office space on University Avenue in Palo Alto. The co-founder and CTO, Ashutosh Garg, was trying to convince me why I should join the company – a place with no production code, no product, no clients and needless to say no revenue. We took a long two-hour walk around Palo Alto when he explained the vision Raj and he had. The most crucial part of their vision was how you can look at it from different time perspectives, and it always made perfect sense. He talked about the 10-year vision, the four-year vision, the one-year vision and – by the way – here’s what we’ll be working on next week. I could find no flaws or issues with any of their plans.

Fast forward to May 2011. I had an important dinner conversation with Raj. It was one of those defining moments that make a leader who they are, and I remember the conversation vividly. Raj laid out the vision for the company very simply and in his typical matter-of-fact manner. He analyzed two scenarios that the still-young BloomReach might face, and was very straightforward about where the company would be and when. Even today, his accuracy and genuineness still blows my mind.

I distinctly recall that a good part of the conversation revolved around where we would be at the end of 2012. His projections weren’t made with rose-colored lenses, but with honesty and hard numbers. I have seen where the company was at the end of 2012. His accuracy to predict all of the external and internal factors downstream was incredible.

Overall, the two co-founders have worked tirelessly together always with 100 percent transparency about their shared vision. The employees of the company like myself and the overall greater community have become increasingly aware of it. Just as an example, recently, Raj and Ashutosh spoke with Elisha Hartwig at Mashable for her article “Five Things to Look for in a Co-Founder.”

Raj spoke about the importance of aligning the mission and passion of a start-up stating, “Often founding teams break down because they ultimately want different things from the venture: one person wants to sell the company early, while the other wants to build a big company. Good teams disagree with each other a lot, but around the important things you have to have shared values around what you want to achieve.”

This type of collaboration is the blood that runs through the veins at BloomReach. All of the teams work together and cooperatively with other teams, often with spirited and healthy debates, to form a common mission that moves the needle in the right direction. That vision, and with a lot of fun on the side, is why BloomReach is being honored as one of the San Francisco Business Times / Silicon Valley Business JournalBest Places to Work” for the second year in a row. Just last week, we were honored at the banquet to  take 25th place overall among quite a few heavy hitters. Although in my book, we’ll always be number one!

We collectively are building a company, technology and brand that will last, and that’s why we continue to grow at an astounding pace, even by the Silicon Valley standards. That growth, though, is tempered with thoughtful planning to ensure that it is always aligned with the vision for the future. It’s no wonder that Raj and Ashutosh are co-semi-finalists for the 2013 Ernst & Young Entrepreneur of the Year for Northern California. Their entrepreneurial spirit and expertise may be steering us through the path, but they’ve created a culture where everyone has a hand on the wheel.

Facebook vs. Pinterest: You’re Investing, But What Are Your Goals?

The rise of social media and feed-based data consumption has created a new channel in which consumers and brands can interact. There are new opportunities for brands to interact with customers to build awareness, cultivate customers, and increase sales. One of the major keys to success is to understand intent and providing consumers with the appropriate information. At BloomReach, we get to see millions of customer interactions per day as consumers land on ecommerce sites from natural search, paid search, affiliate networks, and other websites. By analyzing this data, we gain insight into how consumers express intent, where they are coming from and how they engage with a brand. One of our goals is to take the blindfolds off for our customers.

Facebook vs Pinterest for eCommerce Infographic

For this post, we decided to look at the two preeminent social networks. CMOs report that they are already spending 8.4 percent of their marketing budgets on social media and expect to spend 21.6 percent in the next five years. Our data shows very distinct user behaviors on each network. Recently, we pulled some of that insight for an article and infographic on the popular tech site ReadWrite. I encourage you to read the article, but we thought we’d share a synopsis of the insight on our own social channels.

Our analysis consistently shows that Pinterest has a higher concentration of people who are in a ‘purchase’ state of mind while Facebook users are more interested in interacting with friends and brands.

We analyzed the total traffic – 46,277,543 site visits – for a set of our retail clients during the end of 2012 (Sept. 20 through Dec. 31). Using the last-click method of attribution, we looked at five key metrics from Facebook and Pinterest traffic: total traffic, revenue per visit, conversion rate, bounce rate and average pages viewed. The result – while Facebook delivered more than 7.5 times the traffic, Pinterest handily defeated Facebook in the remaining four areas, even with Pinterest lacking any paid element.

By the numbers:

  • Pinterest traffic spent 60 percent more that coming from Facebook.
  • Pinterest traffic converted to a sale 22 percent more than Facebook.
  • Facebook traffic bounced 90 percent of the time while traffic from Pinterest only bounced 75 percent.
  • Facebook users only viewed on average 1.6 pages whereas traffic from Pinterest viewed an average of 2.9 pages –   representing an 81 percent difference.

The average revenue per visit for Pinterest traffic was north of $1.50, making it a highly lucrative traffic source, and the release of Pinterest’s Analytics Tool for Businesses should help business grow Pinterest traffic in a meaningful way. What is the takeaway for Pinterest? The leads may be better, but the amount of derivative traffic is significantly lower than Facebook.

If a company’s goal is to simply reach a larger audience to create/maintain brand awareness, Facebook probably is your option. The sheer volume of users – estimated to be 1.06 billion active monthly users, 680 million mobile users and 618 million daily users – and the army of people ready to sell impressions make it an easy channel to leverage. However, our data shows you will likely not realize an immediate return on investment.

My advice to those running social media campaigns is to look for ways to optimize Facebook campaign and expand the presence on Pinterest. Facebook and Pinterest should become a larger part of you media mix model as visitor referrals from these sites grow. At the end of 2012, only 2.7 percent of total traffic in our analysis came from either network, demonstrating that social commerce is still in an early stage.

It’s fair to say that Pinterest seems to be a more efficient channel, and the analysis reinforces that key elements of e-commerce are tied to delivering relevant products and overall experience; yet, with billions of ways to express intent, consumers and brands still face a significant discovery divide.

We’re always looking at interesting trends guided by consumer intent and purchasing behavior, but if you have any questions or would like to share our data or infographic for your own purposes, please feel free to reach out to us at media@bloomreach.com.

The Moneyball of Marketing – Big Data in Action

Consumers are spending less time on traditional search engines and more time on shopping engines like Amazon. Thirty percent of all product searches now begin on Amazon as opposed to 13 percent on Google according to Forrester Research, and according to a separate report from comScore, product searches on Amazon have grown 73 percent over the last year while shopping searches on Google have not kept up. This is a trend worrying many companies that are facing an uphill battle getting their brand and products in front of customers. What should companies do to combat this trend; and what should they be trying to achieve to attract the right balance of traffic? Aside from reading on – I encourage you to take a look at our Moneyball of Marketing white paper.

With an unmanageable amount of noise on the Internet and ecommerce forcing many brick-and-mortar retail locations to shrink, digital marketing is the differentiating factor for staying competitive, whether they are online-only or not. According to SEO expert and RKG President Adam Audette, strong brands should be attracting up to 40 percent of non-branded visitors to their website. That percentage may vary by industry, but the point is that if a brand isn’t receiving a substantive volume of non-branded traffic, then it’s not harnessing its full potential.

It’s critical that search marketers differentiate between branded and non-branded traffic to better assess performance and avoid missing new, more valuable customers down the road. Typically, branded traffic is cheaper and converts at a higher rate than non-branded traffic because these searchers seek out your brand and show strong intent to purchase your product. Upon first glance, the lower cost and higher conversion rate might convince some to invest solely in branded traffic. However, this strategy only targets customers who are already seeking you out using your brand, which means that you are missing out on many potential new customers from non-branded traffic that offers a potentially higher lifetime value.

Marketers from strong brands are pursuing the 60 to 40 percent ratio of branded and non-branded traffic because they recognize the value of long-tail visitors, whose net-new traffic spikes traffic by 50 percent or more. Non-branded traffic grows the size of the pie rather than changing how you slice it. In that case, going from 10 percent to 40 percent is actually a 50 percent increase in total search traffic.

Growing the size of your market requires attracting new customers, and those customers’ searches are likely non-branded. However, the ability to attract non-branded visitors is challenging in the growing pool of Web data. Web-wide data is simply too large for people to analyze and act at scale and in real-time, which consumer behavior necessitates. Non-branded searches are often semantic in nature and while its collective volume is massive, the individual terms may only come in ones and twos. Optimization for the long-tail is resource-intensive at the scale, quality and speed required to yield meaningful return on investment.

To effectively attack this problem, one objective should be to optimize reach and demand-attraction, while freeing up marketing resources to return to what they do best – inject the human element of imagination and review. Techniques like natural-language processing and offloading big data analytics to achieve higher performance allows brands to optimize marketing, drive revenue and reach consumers with more focused needs who convert a lot better. The scale and frequency of the non-branded search problem makes it ideal for algorithm-driven solutions and virtually impossible for human; plus, why would you want scores of your best people responding to constantly changing environments producing billions of signals across the Web, on mobile devices and social media? Rather, it takes big data tools like Hadoop and algorithmic intelligence that automatically adapt to changing consumer intent and evolving e-commerce content to maximize the relevant demand attracted to a site while engaging people to ensure quality and brand fit.

It takes an enormous amount of data to analyze and act on web-wide consumer behavior, synonyms and alternative logical ways consumers can describe products, and search optimization best practices.

Many strong brands are using big data marketing in order to capture long-tail discovery. In a marketing world where resources are limited and giants like Amazon have a technology and brand-loyalty edge, companies that embrace their robot co-workers will receive more traffic, better conversions and more importantly, greater sales.

Mobile Discovery Has a Long-Tail

Claire Cain Miller at the New York Times is right – search is changing, and mobile has a lot to do with it. The way we look for things and the extensive choices available present a unique challenge for marketers. With the rise of social media, smartphones, tablets, email, online ads, etc., the ability to connect with the products and services we want can extend to almost any channel that has a connection to the web. In fact, for the first time, RKG found that desktop and laptop clicks were declining year over year, although only by 0.7 percent. Conversely, smartphone and tablet clicks increased year over year by 133 percent and 115 percent respectively. But, one thing hasn’t changed – the demand for consumption and the intent driving consumer choices. Think about it – do you want a dishwasher or a pair of shoes any differently than you did 10 years ago? No, but the ways you can seek and evaluate the available choices on the market have expanded drastically.

For this post, I’d like to look at the channel that Claire wrote about – mobile. It’s a fickle thing that mobile shopper is. The attention spans are short, the tolerance for relevant content is dramatically lower and the presentation of goods for ecommerce companies can be condensed down to a three-inch screen. And, to couple that, consumers are on information overload. The ratio between the noise for consumers and collection of signals for brands is growing further apart to the point where common measures in isolation usually used to determine successful strategies are useless, making it almost certainly a big data problem.

To counter information overload online, consumers are more specific about what they want and where they search. While the New York Times addressed where they search – whether it is on Yelp, Kayak, Amazon or Google, it’s important to note that consumers are also much more specific about what they want – using long-tail searches to give billions of intent signals that smart machines can interpret. Yes, the long-tail is back in search, is critical for SEO and presents itself in a way that human resources just can’t scale.

The long-tail of search is growing and productive for driving revenue, but only those websites that make their content easy to crawl and index will win the click, and even then, only high-quality pages will get the conversion. An overwhelming majority of pages are undiscovered, and e-tailers are leaving a huge chuck of demand on the table by not capturing the long-tail.

The mobile channel is generally more natural-search oriented than the web. The organic percentage of mobile with no referral traffic on a desktop is 39 percent, yet on mobile, the percentage jumps to 55 percent (using iOS 5 as a proxy). This is because 75 percent of iOS 6 organic search traffic is “no query”. As more people move to mobile shopping, e-commerce vendors run the risk of providing poor experiences because of low consumer impatience, intolerance and available screen real estate; plus, the less discoverable you are on mobile devices, the less likely mobile users will visit you on their computers or at a physical location. Not to mention, consumers lose loyalty to a brand quickly, so if you are marketing to those only “inside your tent” without reaching outside – many of which are shopping on mobile devices – you may experience a compounding negative effect.

How do you measure “happiness”?

BloomReach moderated last week’s Twitter #SEOchat, a weekly discussion of hot topics for SEO professionals (mark your calendar for Thursdays at 10am PDT). The topic we covered was “Intent: Matching queries to content (& making searchers happy)”. Huge thank you to Lyena Solomon for pulling together the highlights. There were some great insights from your peers on use cases, metrics and internal ownership of user happiness.

The questions we discussed were:

  1. Does the searcher’s intent factor into your SEO efforts? If so, how?
  2. Do you put yourself in the shoes of a searcher trying to find your products? And do others in your org do the same?
  3. How do you quantify if you are matching intent to the right content? i.e. Are they “happy”?
  4. To turn those insights into action, who is ultimately responsible for making sure search visitors are happy in your org?
  5. If you could have a magic wand, how would you reduce the friction between customer and product (thus making you both happy)?

Have anything you’d add to the discussion?

April Fools’ Special: Top Big Data Blunders

No matter how mature I get with age (don’t laugh – I think it might be true!), the free pass provided by April Fools’ Day is too good to pass up. But, as you get older, the jokes become more light-hearted and seemingly innocuous; however, if you’re like me, I’m sure you can remember one from your youth that left a bitter taste in your mouth.

So is the case with big data: as the industry matures and companies play with ever evolving technology, there have been some epic fails to this point. They’ve torpedoed major launches, wasted loads of money or scared the hell out of consumers. Thus, in honor of our yearly amnesty to commit practical jokes, I thought I’d share some mistakes in big data or those that made us ask, “You’re kidding, right?”

  • The Data Hoarder: Like the TV show, these are the initiatives that start as simple “must-have” information and snowball into terabytes of unstructured data in the same old charts with no additional insight. The IT department hates it, the line of business doesn’t know where to start, and the CFO cringes. Costly consultants come in and implement huge programs that search and compile every stat on Earth, and out spits a report that is either foreign or useless. There’s a point of overload, so you must analyze your internal resources. If you have no data scientist, then employing a solution that gathers big data leaves a wealth of information but no action. In addition, one of the side effects of having too much data could mean you’re a target for hackers. The Online Trust Alliance put together a guide that has practical principles and reminders for determining your data strategy.
  • Correlation and Causation: Tracking trends is one of the most beneficial uses for big data, but sometimes, it just takes a human to intuitively think through the rising and falling graphs. Ice cream sales and increasing drowning deaths may be correlated but it doesn’t take a genius to see that the two don’t cause each other. One heavily cited example was a study by the Proceedings of the National Academy of Sciences that indicted that people who like curly fries are more intelligent. The information alone is an interesting correlation, but without greater context about the cause, it leaves questions. At BloomReach, we strive to understand the “why,” not the “what.” We focus on vast amounts of factors to determine intent and predict outcomes, not just track ebbs and flows in charts.

temp vs pirates

  • The Underground Burger Economy: Ever thought buying a burger would cause a rise in your health insurance rates? That’s exactly why a data mining executive  buys his fast food in cash, to avoid being a victim. At first glance, you may think this is a little paranoid, but these types of scenarios are very real, and the entire big data industry should exercise extreme caution. Consumers are becoming increasing aware of the term “big data,” and the entire industry runs the risk of creating a backlash. Data is good – but responsibility with data is better.
  • Facebook Beacon: Well wasn’t this a beacon of darkness. Your purchase – tracked, posted and shared. So much for the Holiday season or maybe a little secret indulgence. The big-data blunder cost Facebook a PR scar and a $9.5 million settlement in 2009.

Let’s face it, competing and making money at the scale and speed of technology is going to be data-driven. But, as we grow as an industry, we have to remember that people created the technology – meaning that people should have a place in its quality assurance and application.

Image by Flickr user pathfinderlinden used under Creative Commons license.

Discussing Dumb, Scary or Useful Big Data

In a recent article I wrote for WIRED’s “Innovation Insights”, I discussed three different categories for big data initiatives: dumb, scary or useful. As the term “big data” becomes more prevalent in the general population and continues to develop in the world of enterprise, many companies treat the overwhelming wealth of information available the same way that a person who quickly comes into a sum of money acts – either with poorly conceived wastefulness, borderline unethical behavior or for a long-term, practical strategy.

I’m sure we all can relate to the first category, big dumb data. How many times have you been followed around the web by targeted ads that were only relevant at one point in time or not at all? You purchase a hair dryer, and all of a sudden, some appliance company wants to sell you a washing machine or a clothes dryer in every banner ad – all because some automatic software recognized your search query. Or, how about seeing banner ads for your own company? These examples underscore the importance of intent and context within your online behavior, and many companies fail to realize that stalking you with irrelevant offers only turns you off to a brand – definitely not the intention for which they were hoping.

Big scary data is a point-of-view problem that our industry must combat. The articles appear weekly – is big data akin to big brother? In many cases, it can be, which is downright scary. E-commerce companies have a responsibility to collect and use data appropriately. We aren’t talking about selling consumer intelligence to third-party marketers. We’re talking about a hypothetical situation where a data miner detects a consumer’s searches for a deep fryer, and the information finds its way to their health insurance company interpreted as a bad habit, as suggested by Dr. David Vladeck, professor of law at Georgetown University in a recent New York Times article.

The emergence of big data technologies is designed to help make our lives easier, connect us better with the products we want or have a great impact on society. Big useful data is the type that understands the intent behind your actions. It processes the actions you take and makes suggestions with which you are likely to engage. At BloomReach, our intention is to give companies the opportunity to get in front of the audiences they want. Our engine deciphers and predicts the intent behind online shoppers’ queries and matches it with our customers’ content – which can be buried beyond the reach of some search engines. It’s a simple equation: you want it + our customers have it = happiness for everyone.

“It’s about relevance for the consumer and revenue for the brand”

I had the privilege of being interviewed by The eTail Blog‘s Kelly Hushin at eTail West in Palm Springs earlier this month. eTail, as always, was a great event for innovative retailers and vendors in the space. At one point in the interview, Kelly asked me to explain BloomReach’s efforts to help companies “win the relevance race”. My response…

It’s about relevance for the consumer and revenue for the brand. If a retailer can help the would-be-customer find what they are looking for, they both win. That’s our goal.

The Gift of Intent

As any ecommerce company or child during the Holidays knows, gifts are a happy experience; but, it’s important to remember that it is the intent that counts. In a recent post by Eric Enge on Stone Temple Consulting’s blog, we supplied data and analysis about “gift” search queries which underscored that making assumptions based on knee-jerk conventional wisdoms can cost you badly.

After looking at nine retailers’ data related to gift searches – which included terms like “gift” or “present” and even phrases like “Valentine’s Day chocolates” – we found that most of the retailers experienced a higher bounce rate than all other queries. In fact, the bounces increased by as much as 331 percent. A common supposition is that optimizing a page specifically for gifts results in low-quality pages, but analyzing this metric in isolation would have given a bad gift to the CMO. Why? Because, in many cases, it literally pays to take an extra moment to couple one metric with another, and in this instance, that metric was conversion rates.

Looking at the same set of companies with the same data set, gift queries converted better than all other queries for all nine retailers – some considerably. What does this indicate? There’s a little bit of psychology backed by hard data-driven takeaways when searching for gifts online. As we all know, browsing the Internet isn’t like walking through the aisles of a store – usually there is a somewhat specific starting point for online shoppers. They demonstrate strong intent because they have an idea of what they’re gifting and acknowledge that the Internet is unorganized. In one instance, we noticed and recognized “housewarming wine basket” as a very descriptive gift query, but not one easily categorized as a gift search without some natural-language processing technology. So, you see very intentional and descriptive search queries, which is marketing gold. Consequently, the experience comes with lots of impatience, because if the right item is not presented quickly, your potential customer is out of there with one click of the “back” button.

We’re not implying that you should go and optimize your site for all kinds of words associated with gift queries, not in the slightest. Those searches made up no more than 1.18 percent of all queries for these nine retailers. However, thinking about common online marketing metrics requires more intuitive thought, and optimizing for one metric can lead to unfruitful decisions. The same phenomenon that we see with gift queries can manifest itself for all queries with strong intent. For example, “products for clogged sinks”, “sinus relief medicine”, or “dresses for a beach wedding” each express a very strong intent.

As I told Stone Temple, determining which queries have strong intent and capturing that intent with relevant pages is the key to improving conversion rates for e-tailers.

Unlocking the value of content on Meijer.com

At the University of Michigan, every student knows that the one-stop shop for anything you need for your dorm or apartment is Meijer. Meijer wanted to bring their 75-year history (the original supercenter retailer) to the national stage through eCommerce and to maximize the number of new customers who discovered Meijer.com through natural search, they turned to BloomReach’s BloomSearch product.

I remember wandering through the aisles of Meijer being amazed that a store carried groceries, furniture, clothing and auto-supplies. But the Internet offers so many choices and Meijer stands out with their continuously growing product offerings. BloomSearch ensures that their new products get found through relevant in-site links, rich site content and new category pages.

During the Internet Retailer webinar – Unlock the Value of your Content – Meijer joins BloomReach and Forrester Consulting to share their results. Forrester just completed a Total Economic Impact study on BloomReach Organic Search consolidating results across multiple customers – and finding a 196% profit-based return on investment and 2.2 day payback period for established brands and over 600% ROI and 1 month payback period for emerging brands – or brands like Meijer.com that are well established in a particular market and want to expand nationally.

Big Brains: Alon Halevy on structured data and coffee

Last week, we hosted the 1st Big Brains speaker at BloomReach headquarters. Big Brains is a speaker series where we’ll invite brilliant minds in Silicon Valley to share their ideas and insights. Google’s Alon Halevy got the ball rolling with a wonderful discussion of structured data on the web and a bit about his passion for coffee (which believe it or not, provides a great example of structured data at work play. And if you’re a java lover, Alon’s book, The Infinite Emotions of Coffee, is a great read.)

Watch the replay of Alon’s talk. And like the Big Brains Facebook page to stay informed about future events.

Interpreting True Value

Estimating the true value of anything is tough. As a working mom, I value bagged lettuces in the grocery store so much more than a less time-constrained person might. The true value to me is probably equal to or more than the price on the shelf since I know that I won’t have to cut, wash, drain, and dry the lettuce before the madness that is dinnertime. Someone with more discretionary time might see bagged lettuce as a waste of money – why not just do it yourself? Clearly, these values don’t match.

Recently, BloomReach commissioned a study with Forrester Research to evaluate the true economic impact of integrating BloomReach’s signature technology, BloomSearch. What was amazing about the findings across the board is that large or small, established or emerging, all brands agreed that the true value of BloomReach is extraordinary. No matter what the goals of the companies who participated in the study were, each said that the benefits of BloomSearch, enhanced discovery and conversions, were worth the investment, albeit a small investment.

The study concludes that a composite, established brand retail organization selling $3.5 billion annually from a portfolio of 25,000 products could experience a three-year return on investment (ROI) of 196 percent with a payback period of 2.2 days by using BloomSearch. In addition, the study found that organizations using BloomSearch experience a substantial increase in site traffic from unique visitors and reached 60 percent more customers that are not easily accessible through other marketing channels including paid search, display, or affiliate advertising. For established brands, BloomSearch doubled conversions compared to paid search.

The value is even better for emerging brands (an online retailer making $250 million annually) wherein for more than $75 million in new sales over three years can be attributed to BloomSearch. These sales have a return on ad spend of $5.85 and a payback period of 1.2 months. BloomSearch also accounts for tripling conversions compared to the paid search efforts in which the emerging brands invest. The emerging brand companies interviewed in the study credit higher conversions rates than paid search, brand-building at a lower cost, and the ability to acquire new customers that are not being reached through other channels as the main drivers for the success of the program with BloomSearch.

BloomReach’s CEO Raj De Datta alongside large retailer and BloomSearch customer Meijer and an analyst from Forrester will address the TEI study and also talk about the discoverability problem that BloomSearch solves at an Internet Retailer webinar on Mach 6th @ 10 AM PST. We hope that you can join us.

Follow this link to download The Total Economic Impact Of BloomSearch.