P&G’s eStore was prompted by a mediocre year in online sales for the consumer goods manufacturer. According to a recent article by Andy McMains of AdWeek, less than one percent, or $500 million, of P&G’s $79 billion in global revenue last year stemmed from online sales via sites such as walmart.com and amazon.com. Through the eStore, P&G hopes to learn from consumers how to better market their products and improve online sales across all e-commerce channels. One of the main goals of this initiative is for P&G to learn about consumer buying patterns and share this customer intelligence with its retailer partners to boost sales.

eCommerce is unique in that Websites allow retailers to quickly identify consumer shopping patterns, and automatically adapt their merchandising programs based on this insight. This is simply not possible in brick-and-mortar stores. With Baynote’s technology, for example, retailers such as Urbanoutfitters.com and Bluefly.com are able to more quickly recognize even the most unexpected trends, such as consumers purchasing two seemingly unrelated products, and then personalize product recommendations in real-time. This improves the next shopper’s experience, builds loyalty and boosts sales.

Since most products are currently sold via retailers rather than directly by manufacturers, manufacturers are often left in the dark on the valuable data that their retail partners can obtain about consumer behavior. While brands have been able to utilize the Internet well to engage with consumers via email marketing, social networks, online product reviews, and a slew of other activities that require their explicit feedback, they have not had a direct link into the less obvious but often more telling data that ecommerce affords. This includes leading, often termed “weak” signals about consumer intent that can only be gleaned from the online shopping experience. Think clicks and hovers, not hard transactions.

This new online environment should enable P&G to monitor and respond to shifting consumer trends in real-time, and we are looking forward to seeing how their customer engagement initiative will change the overall online retail landscape. We predict that the insight gained from eStore will improve both indirect and direct sales, as P&G will be able to better collaborate with their distributers on why certain products are selling well, and how to improve upon the merchandising and online sales strategies. If managed well, the P&G eStore should also allow the manufacturer to bring innovative new products to market significantly faster based on real-time visibility into the online browsing behaviors of their customers.

6Sense developed out of a 2008 acquisition of search technology company Trovix.  According to a Monster press release, “6Sense patented semantic search technology utilizes intuitive, concept based searching, with a human-like understanding of the recruiting process and hiring needs.”^[1]

In case you’re not familiar, semantic search utilizes a concept called “disambiguation” to determine the probable meaning of sentences by building conceptual relationships between words, syllables, and related text.  However, the system cannot function in absence of what’s called the “conceptual basis” or primary logic required to begin building the conceptual relationships which govern the system.

But who or what determines the logic of the conceptual basis for a given semantic search system?

Intuitively, most people respond with an answer they find very simple, “The task at hand determines the logic of the conceptual basis”.  However, this simple and intuitive leap of the designer (the human mind) is an exceptionally difficult leap for the designed (computational system).

Unlike its designer, the semantic search system does not have the luxury of thinking outside of its own box.

Because the system relies on the wisdom of its designers to set the most appropriate logic, would it make sense to open up this process to a larger audience?

Given the presumably enormous amount of data and users, how many possible sub-contexts exist within the contextual premise of the initial system?  What is the probability that a conceptual basis created by a few experts will account for all of these possible combinations?  And most importantly, would differentiation at this level of granularity deliver measurable improvements to the user experience?

Well, I assume some level of diminishing marginal returns would arise; however, I’m confident this level of contextual granularity has probably not been reached in the case of 6Sense.

At the very least, I think this ad clearly communicates that successful companies are coming to realize the importance of their web channel, and therefore, the importance of relevant search and navigation.

And when it comes to relevance, we’re all beginning to realize that context is king.

I think Monster said it best. “The intelligence of the 6Sense technology contextually interprets the meaning behind words and concepts rather than relying on the narrow, literal meaning of keywords.”^[2]

I couldn’t agree more.

http://www.businesswire.com/portal/site/home/permalink/?ndmViewId=news_view&newsId=20100201006672&newsLang=en

According to Gartner, a pattern-based strategy “provides a framework to proactively seek, model and adapt to leading indicators, often-termed ‘weak’ signals that form patterns in the marketplace.”  For the past several years Baynote has been committed to helping companies identify these patterns with technology that lets them tap into the collective intelligence of customers visiting their websites. This is something that transactional based systems such as business intelligence (BI) and complex event processing (CEP) simply haven’t been able to deliver. Here’s why:

1) For years BI, CEP (more recently) and other related technologies have helped organizations become much more efficient by automating their interactions with customers. However, in the process of creating huge economies of scale, they forced companies to lose the “mom and pop” touch that consumers expect when they walk into a local hardware store or restaurant. In failing to create digital mom and pop experiences, online retailers and publishers have placed unnecessary emphasis on promoting popular products and content, thereby losing out on profits to be gained from merchandising their long tail products.

2) In addition, these so-called “predictive” applications have historically prioritized the wrong set of indicators, often identifying consumer trends weeks, if not months, after the fact. For example, e-commerce transactions lag other more relevant indicators, such as online comparison shopping, by months. Only by tapping into the power of the collective is it possible to see early signals, spot trends and develop strategies around them before your competitors catch on. This holds particularly true for long tail products. Our customer US-Appliance tapped into the implicit behaviors of its website visitors to merchandise colored washers/dryers months before Home Depot and Best Buy began promoting similar products in their stores.

In Gartner’s recent report, entitled “Introducing Pattern-Based Strategy”, they view “the collective” as being critical to developing a pattern-based strategy. We couldn’t agree more with their position:

The collective comprises individuals, groups, communities, mobs, markets and firms that shape the direction of society and business. The collective is not new but technology has made the collective more powerful — and enabled change to happen more rapidly. The explosion of social software has enabled groups and individuals to rapidly form and rally to a cause — often resulting in significant societal changes.

The result for business is a cacophony of rapidly evolving demands, expectations, inputs and transactions, as well as an opportunity to not only react, but to seek signals of change from the collective. Market trends, some subtle, others strong, are masked by noise, and many enterprises are failing to proactively detect the patterns they rely on to direct future strategy and support investment decisions. In addition to failing to detect these patterns, enterprises are not utilizing new resources to proactively seek signals of change nor do they understand their power to influence individuals and communities.

Val Sribar, group vice president of Research at Gartner, sites Amazon’s and Netflix’s use of recommendation engines as good examples of organizations leveraging collective intelligence to support their pattern-based strategies. Sribar agrees with Baynote that recommendation engines identify new patterns in behavior as customers browse and purchase. While Amazon and Netflix are highly popularized cases, we’ve helped hundreds of other well known brands tap into their collective customer networks to significantly increase revenue through cross-selling and upselling, and higher customer loyalty.

We’re excited to see Gartner take a leadership position on this important issue and look forward to working with them and our customers to bring best practices related to collective intelligence to the forefront of modern business strategy.

1. The critical information is not in the document

While documents — whether webpages, pdfs, or Word docs — seem like the best place to discover a match to a user’s search term, they’re not. Processing documents is a good start, but the words within a document do not necessarily match the way a user understands the topic and phrases their question. And even if the search term is in there, it doesn’t mean that particular document is useful. The critical information is in the heads of users, not the documents. The key is to understand how, when, and why people use each document. At Baynote, we call this UseRank.

2. Actions speak louder than words

To get information from users you might think the best approach is to ask them. Seems simple and straightforward, right? Wrong. Turns out that there are a number of problems with explicit means of collecting information stemming from who participates, when, and why. As social science has taught us all along, if you really want to understand people, watch what they do, not what they say.

3. Search does not exist in a vacuum

Any time someone comes to your website, they are looking for something and they give you clues to what that is through both their search and navigation behaviors — and not just what they ask for and where they go, but what they do when they get there. Often they got to your site through an external link such as a search on the web — that’s your first clue. Although the goal might be to solve the site search problem, observing search behavior alone is not enough.

I’ll expand on each of these in more detail in upcoming posts.

The report, Americans Reject Tailored Advertising shows that 66 percent of adults said no to tailored ads. Not only that, when informed of specific behavioral targeting techniques that marketers employ to create the ads, even higher percentages — between 73 percent and 86 percent — oppose tailored advertising. Those techniques include tracking behavior on websites and in retail stores.

For a more detailed analysis of the findings, you should check out Stefanie Clifford’s coverage of the report in the New York Times.

We’ve been talking about the pitfalls of behavioral targeting for years, so it’s nice to finally see some national research that tells marketers what consumers actually think about this shady technique. In this age of identity theft and mounting concerns over privacy in general, a practice that proactively profiles a user — perhaps over the scope of many Web sites and over a period of several months — will sound alarms even among the least conservative of us.

Beyond privacy concerns, there are bigger issues with behavioral targeting related to accuracy and quality, that many marketers still don’t understand. Traditional behavioral targeting struggles precisely because it tries to discern what I want now based on my past behaviors. Consider the impact of focusing on historical interests instead of current intent: If I bought a gift for my niece on Amazon.com last week, I certainly don’t want to be bombarded by ads for similar products that probably aren’t relevant during my next visit.

Another way to think of this problem is to consider the idea of roles or what personalization systems might call “profiles”. Humans have far too many roles in life for a profile to possibly predict what a user wants on any given day. A woman shopping for baby clothes, a tie for her husband, and a gift for her sister may appear schizophrenic because she is acting in three different roles — mother, wife and sister. What do you show her next? Tossing strollers ads at her isn’t going to be effective now that she’s shopping for a new cocktail dress for herself.

This is the pitfall of profiles. In a given month, an individual will have thousands of roles. Knowing my past is not necessarily a better way to predict my future. In fact, this phenomenon has been known by psychologists and other scientists for years — humans are animals of context and situations, much less than of historical profiles or roles.

Enter Intent-driven Targeting

An alternative that solves the issues with both privacy and effectiveness is one centered on understanding the users’ intentions, instead of their clickpaths or profiles, and pairing that knowledge with specific content, product and advertising recommendations. This approach relies exclusively on the collective wisdom of like-minded peers who have demonstrated interests or engagement with similar content and contexts.

The concept of profiles is completely removed in this case. Instead, through understanding expressed or implied intent, content appropriate to the user’s current mindset can be delivered.

Most importantly, it kills two birds with one stone: Users get useful, accurate recommendations and ads, while still avoiding the whole privacy mess.

The post, written by YouTube product manager Shiva Rajaraman, explains that the majority of YouTube users who rank videos give them a perfect 5-star ranking. He continues:

Seems like when it comes to ratings it’s pretty much all or nothing. Great videos prompt action; anything less prompts indifference. Thus, the ratings system is primarily being used as a seal of approval, not as an editorial indicator of what the community thinks about a video. Rating a video joins favoriting and sharing as a way to tell the world that this is something you love.

Rajarman goes on to solicit the community for feedback on how useful the current ranking system is and what can be done to improve upon it.

We’re really glad to see that YouTube is finally examining its rating system with an eye on delivering more value to its community and look forward to seeing how the system evolves from here. Ratings and user generated reviews, though often misleading, have become an expected part of the online experience and encourage deeper engagement. I don’t think anyone would take away points from YouTube on their ability to engage an incredibly large, diverse and influential community of users. However, YouTube’s review system- and others like it –  must also find ways to inform ratings based on valuable sentiment and implicit feedback gathered from the vast majority of their site visitors. Not the loud minority.

With a truly integrated approach to recommendations that blends both implicit and explicit feedback, companies can expect to improve engagement and overall user experience by directing site visitors to the best content based on their intent.  I talk a lot about this concept in my paper, entitled “7 Deadly Biases”.

In the end, explicit versus implicit user feedback shouldn’t be viewed as an either/or scenario. Please let us know your thoughts on the matter and share examples of sites that are doing it right.

Is Relevancy and Privacy Inversely Related? No.
So here is the question, Can we target users effectively without diminishing their privacy? Does increased targeting inherently scale with lesser privacy? I would say no. Google has made their fortune on contextual targeting. Users provide Google with questions and Google matches those questions with two types of answers, those derived from their organic search algorithms and those offered by advertisers. The success of this solution marked a victory for contextual targeting over the profiling. Google effectively targets content to users by understanding the context and intent of users, exactly what Baynote does as well. No profiling or personal preferences were required.

This topic is a bit much to cover in one post, so expect a few more posts to follow up on why relevance and privacy are mutually exclusive.

When I was pregnant,  Amazon kept recommending me books on exotic locations my husband and I had planned to travel like Cambodia or Patagonia.  Oh cruel Amazon and you won’t even fetch me ice cream!

I haven’t done a lot of shopping this year on Amazon.  If I had it would start showing me crazy recommendations based on some esoteric book I bought my physicist stepfather.  That was his wierd niche, not mine, not mine!  Let it go!

So this morning, I log on and what do I see but an Amazon recommendation for a book on losing weight while breastfeeding.  Not only is my youngest now a year and a half and I am waaaay past that phase but it seems, again, eerily cruel that Amazon finds a way to remind me that during the holidays I am eating more than I burn.  Now that’s just cruel.

Yesterday, one of the conference attendees that visited our booth created a blog post explaining her take on targeting technologies, but on a personal level.

My favorite part of Baynote’s technology: it ignores demographics. Yes! When are people going to figure out that this is an individualistic age? Advertisers, you are wasting your dollars on serving me with endless weight-loss and dating ads. Not all 27-year old females are the same!

I’d have to agree with this point, not all 27 year old females are the same. Not simply because I’ve seen our technology benchmarked against demographic or profile based targeting technologies(which I have), but also because my 27-year old wife would have me sleeping on the couch if I said otherwise.

If you are a new reader, or would like more background on our contextual targeting approach, check out our whitepaper “In Search of The Human Element.”

Fortunately, Baynote Recommendations uses the wisdom of the crowd in such a way that does not rely on this kind of information for intent driven recommendations. Our secret sauce doesn’t require users’ location or personally identifiable information as an ingredient to power our recommendations. Check out how it works here.