Market basket analysis, or MBA, has been a staple of retail marketers and merchandisers for decades.  In a nutshell, MBA allows retailers to see what line items and promotions a customer’s “market basket” (or invoice) contains, and then look at trends across regions, categories, customer types, or other attributes.  Basic MBA will tell a grocer, for example, that if there are cookies in a basket, there is usually also milk.  Conversely, it will also show that if there is milk in a basket, there is no correlation with cookies also being in the basket.   The grocer can then infer that cookies drive sales of milk, but that milk does not drive sales of cookies.

That is great, basic information and has been the extent of retail MBA for years.  But that’s where the traditional value of MBA starts to fade and where the complexity of the SQL queries starts to increase dramatically.  Retailers are thirsting for more customer insights and the next generation of market basket analysis, where multi-attribute, finer-grained questions can increase profits, better target customers, and exploit both the masses and the long tail of customer purchases and demographics.

If a marketer wants to put together a promotion directed at woodworkers, they might want to know which customers frequently purchase sandpaper and saw blades together, have purchased premium hardwood in the past 90 days, and have purchased paint products in the past six months.  They can also look at customers who have purchased wood stain in the past 30 days, and then look at the purchases of those same customers for the preceding 90 days to determine what leads to a purchase of high-profit stain.  However, with traditional business intelligence tools, this type of analysis becomes an exercise in patience, requiring complex queries and days if not weeks of time to actually get the insights.  And it’s still not “advanced” MBA.  It’s these types of questions – easy to ask, difficult to answer – that have retailers yearning for a better solution.

Going much further, the explosion of data collection systems, potential customer interaction points, and channels has dramatically increased the potential marketing opportunity available to retailers – but the available analytics technology has not been able to keep up.  A decade ago, it was fine to just perform simple MBA on your point-of-sale data.  Five years ago, maybe you looked at both POS and e-commerce data (probably separately).  Now, you’re wondering how to integrate all of your channel data, pull mobile and social data in, and really uncover who your customers are, why they are doing what they do, and which attributes are important for your specific business or campaign.

Quantivo’s advanced market basket analysis is specifically built to answer these types of questions and combine all types of customer data easily, quickly, and with existing resources.  Yes, the utopia of integrating social, POS, e-commerce, customer service, and mobile data is possible, but most retailers simply want to improve their market basket proficiency:

• Want to increase lift?  Quantivo helps you uncover how items sell over time, how frequently, to whom, via which channels, on which payment mode, by which delivery mode, and on what days.
• Want to increase impulse sales? Quantivo helps you classify shopping trips based on items purchased, time of day, and frequency so that you can merchandise for dinner shoppers, wedding attendees, home remodelers, or other “trip-specific” customers.
• Want to maximize margins and optimize discounts?  Quantivo helps you identify the “purchasing threshold” for specific customers, items, or segments so that you know what will get customers to visit and purchase more often.
• Want to better plan store layouts?  Quantivo lets you accurately understand and exploit store-specific opportunities by comparing locations with different characteristics with normalized behavioral metrics.

It’s our design that makes Quantivo so well-suited to helping you advance your market basket analysis capabilities.  Our focus on patterns and associations is ideal for purchase analysis.   Our innovative database was designed for modern data and today’s multi-channel, on- and off-line customers.  Our entire solution was designed to take advantage of today’s modern technologies to offer incredible analytics speed, a fast and painless deployment, and a budget-friendly price.

What do you think?  Let us know.

If a customer visits your website, clicks on the “find a store” link, then logs off can you tell if they ever actually visited your store?

The response for most companies is, “No, because we don’t (or can’t) integrate our web analytics data with our point-of-sale data.“  Why?  Because even though all of the data lives in their data warehouse, or is easily accessible in their individual systems, traditional business intelligence tools are not good at data integration and are even worse at allowing a full, 360-degree view of data across multiple systems.

But why is it so difficult?  Customers have a unique user name for your website and have a unique credit card number that they use in your store (or account number, or loyalty card number, or other identifying code). And many retailers ask for your email address when you’re purchasing something in the store. The data is there, it’s the integration that’s difficult.

One of our customers – a Fortune 500 company that owns a blimp – uses Quantivo to not only connect their web data with their sales data, they go even further to connect their marketing data with their CRM system’s lead owner, sales stage, and product data.  And since “attribution” is the key buzzword in online marketing these days, their ability to connect first touch to actual revenue makes attribution as easy as running a simple report.

Here’s a quick example of what they look at in Quantivo every single morning:

From their advertising department, they integrate television ad data into their behavioral analytics.  If they had a TV spot that aired at, say 9:22 pm yesterday, they attribute the bump in traffic over that time slot’s average to the TV spot.  In other words, if they usually see 1,000 unique visitors in the 9-10pm slot, and yesterday they saw 1,200, then they attribute those extra 20% to the ad.   For other marketing programs, like online ads and email campaigns, and for general online behaviors, the data is already collected in their web and marketing automation systems, so the attribution is a solid connection. All of this data is fed into Quantivo.

Quantivo lets them dig into that “pre-sales” data to understand what drove a prospect to the website, what content or products were viewed, if they returned (and from where), how many times they visit, etc., all with the goal of answering, “What drove them to click, ‘Ready to Buy.’”  Now this is where it gets really interesting!  Once a prospect clicks to contact a sales person and enters their information, Quantivo kicks off a process that sends the data to their CRM system, creating a sales lead and generating a task for the appropriate person.

Even better, the CRM data is then pulled back into Quantivo for the deep, click-to-close analysis of each stage in the sales process, from “initial contact” through “products purchased” and the accompanying revenue.  With these insights, not only can they understand the complete and amazingly accurate ROI for each marketing campaign, they can further understand how to nurture prospects who share similar traits (e.g. originated from same campaign, viewed same web content, etc.) but have yet to purchase.

This complete “marketing-to-revenue” analysis has been elusive for decades.  Companies relied on their data warehouse to centralize the data and assumed that the analysis would then be simple.  Obviously, that hasn’t been the case.  Billions of data points, complex analytics tools, and the relational approach to data has been hampering the effort to answer the simple question of, “What should I do next?”

Finally, that problem is solved.

The paint is barely dry on our summer enhancements to Quantivo 4, but we’re already getting great feedback from our long list of customers!  While Quantivo 4 already puts never-before-seen analytics power into the hands of analysts and savvy business users, these summer enhancements are like a tall, cool glass of fresh iced tea on a hot summer day…or some other summer/heat/refreshing analogy.  (Maybe our codename should have been “mojito?”)

Here’s an overview of a few of the new capabilities, but you can get more details in our “What’s New” data sheet or in our press release.

NEW: PRECISION BEHAVIORAL TARGETING
Quantivo 4 users can query customer data by combining any set of attributes within the same dimension. The types of attributes are unlimited and can combine traditional demographics such as age, gender and location with behavioral attributes such as web pages viewed, products purchased, frequency of visits, promotion or campaign response and date of last activity.

NEW: LINE-ITEM CONTRIBUTION ANALYSIS
Understanding each line-item’s contribution to the total is now easily available across any dimension. For example, each products’ contributions to total market basket size, or the number of minutes spent on each page in relation to total session times, can be easily added to any report.

NEW: ENHANCED STATISTICAL ANALYSIS
View behavioral data from multiple statistical perspectives directly in the results. In addition to the standard aggregations, new capabilities now enable additional statistical measures for even faster identification of valuable opportunities.

For details on the complete power of Quantivo 4 and what our revolutionary behavioral analytics solution can do for you, visit the Quantivo 4 product page.

We’re increasingly frustrated with the continued focus on decades-old database, data warehousing, and general business intelligence technology.  Salesforce.com took a stagnant solution, CRM, reimagined how it could be architected, delivered, and priced, and turned an entire industry on it’s head (and pretty much drove Siebel Systems into the grave – I know because I was there).  Well, we think that it’s time to do the same with the analytics world, and we’re going to continue challenging those preconceived notions around what companies should expect from their BI tools.

Which brings us to a recent blog post at Occam’s Razor by Avinash Kaushik, who is the “Analytics Evangelist for Google.”  The post discusses “10 Fundamental Web Analytics Truths,” and I’m sad to say the basis for, and the thought process behind, most of them are misguided, antiquated thinking that is just flat out wrong! Furthermore, Avinash’s unwavering focus on simple metrics and aggregated data is the prime source of inaction and analysis paralysis in companies today. Businesses need INSIGHTS not more high-level statistics or dashboards. Yes, metrics are required to measure progress, but they do little or nothing to tell you HOW to move the needle in your business.

Avinash is the most off the mark on his #7, where he states that most web analytics data warehousing efforts fail miserably. While that statement is true, it’s because most data warehousing efforts are based on tired old technology that just can’t handle the massive amounts of data being collected by web and marketing tools today, and that don’t take full advantage of modern approaches to computing, namely the cloud. Today’s businesses require new thinking around analytics from the ground up, not just the same old relational database approach jammed into a “massively parallel appliance.” That’s just lipstick on a pig.

Data warehousing failures are definitely not because of the six sub-points that Avinash makes. Saying that “there is too much granular data” is completely missing the point – or probably supporting Google Analytics’ focus on only allowing their users access to aggregated data. In order to make effective decisions AND have the ability to act on them, marketers NEED the granular data! All of it! Aggregated, summarized data is great to see trends, but then how do you capitalize on them? How do you know which clicks, which content, and which customers are the ones that matter? Without the granular data, you’re taking a shotgun approach to marketing. You’re basing strategies on high-level statistics instead of letting your customers’ actions tell you exactly how to move forward.

Avinash also writes that logical structures and relationships hardly exist in web analytics data. That’s an incredibly confusing statement. Web data is fundamentally structured into sessions, users, dates, times, etc. It is amazing the amount of insights and behavioral patterns that Quantivo customers are finding within their Webtrends and Omniture data. Or, maybe Avinash is just referring to Google Analytics aggregated data and the lack of any ability to actually find patterns?

His conclusion on data warehousing efforts is one of his few sound opinions, however. No company should be spending millions of dollars and months/years of time for a data warehouse when other approaches are available for orders of magnitude less, and can be implemented in just a few days.

On his #8, he writes that a solution for multi-channel analysis does not exist. That is, again, just flat-out wrong. Quantivo has proven again and again how easy it is to merge different data sources: Retailers merging web, POS, and catalog data; services companies merging marketing, web, and CRM data; media companies merging web and demographic data; software companies merging web, CRM, and call center data. We’re not offering a “magic bullet,” just a modern approach to a decades-old problem that produces real results. And yet again, Avinash has his old-school BI binders on and is not even considering a different approach to the problem. At Quantivo, we’ve solved it.

His #10 suggestion to publicly embarrass HiPPO’s (“the Highest Paid Person’s Opinion”) is probably the most short-sighted and misguided of his points. When is it ever a good move to embarrass someone in a business scenario, let alone someone who is your superior? I know that I would never use that as a tactic to press my objectives, and I’d never want someone to do it to me.  I’d want the best decision to be proven out by considering customers across multiple interaction channels, with deep insights into their behaviors, and the granular data to put the ideas into action.

At Quantivo, we strive to make our customers heroes in their company and in their space by making them incredibly successful. How? By giving them easy access to discover what makes their customers do what they do, taking granular data and letting them quickly execute on the insights that they find, and by giving them the ability to quickly answer the questions that will push their business forward.

Skeptical? I’m sure that you are. If you have customer data, let Quantivo prove it to you today.

A lot of the marketing and web analytics tools out there today position their segmentation capabilities.  They talk about finding new segments, increasing the value of existing segments, and even simplifying segments.  But what they fail to say is that segmenting on the same old attributes is not going to have much of an impact on your marketing success.

Without an analytics or segmentation focus, most companies are targeting based on broad metrics, like pageviews or campaign response, or sales by region.  They then take the shotgun approach and blast out a message that hits such a wide swath of prospects, they are doomed to thinking that a 2% hit rate is successful.

Even a more “advanced” approach relies on the same segmentation data that has been in use for decades:  demographic data.  Customer 456 is female, lives in Chicago, is 18-30 years old, and has a household income of $50,000 – $75,000.  Fantastic information, for sure, but is it enough to know if customer 456 should be a part of any segment?  Does this person race in marathons?  Buy clothing that is usually blue?  Get referred to your website from music sites?  Travel to beach destinations for long weekends more than five times per year?

Without behavioral analytics, you have absolutely no insight into whether or not customer 456 should be a part of your segment, regardless of why you are creating that segment.  Knowing only broad metrics or demographic data does very little to help you drive clicks or purchases or profits.  You are still taking the shotgun approach (albeit with a choke, for all you hunters out there), and basically gambling with your marketing, hoping that you hit enough of the “right” prospects to result in a marginal up-tick.

However, if you knew that customers who spend more than $100/mo with you, browsed online for wine glasses more than once, and are usually referred from sports sites have a high propensity to purchase top-end toasters, wouldn’t you want to capitalize on that segment?  And, since you are already collecting that data, shouldn’t you be able to easily create a list of customers who exhibit all of those behaviors?

Behavioral analytics allows you to do exactly that: first, understand the behaviors that lead up to your desired behavior, then identify the segment that has exhibited most of those behaviors but not the desired behavior.  The killer feature is that behavioral analytics lets you begin with a blank slate.  You don’t need to know in advance that you want to target on people who fly to London and stay in a four-star hotel more than three nights per quarter.  How do you know that those people are valuable or even profitable?

Behavioral analytics let’s you focus on your desired behavior, then work backwards to discover all of the behaviors that lead up to that desired behavior.  Then you can easily and confidently segment on the customers who are ripe for an incentive to lead them to your desired outcome.  If behaviors A, B, and C usually lead to D, then segment those who have done A, B, and C but not D, then push them to D.  Next, segment those who have done A and B and incent them to move to C, and so on.

Behavioral analytics goes far beyond standard analytics and simple segmentation.  It gives you the ability to DISCOVER which behaviors and behavioral patterns are valuable, the INSIGHT to uncover which behaviors are actually associated with that desired action, and the ability to unearth the DETAILED DATA that enables you to instantly identify which customers make up your segment and then target them directly.

Want to learn more?  Click here.

I just read this short article at Information Management which focuses on the increasing BI and analytics issues related to exploding data growth.  Since we’re all data geeks, let me just focus on the numbers they presented from a recent report:

• 87% of companies blame performance issues on data growth
• Databases increase in size by 50% per year
• 35% lack a grasp on how to manage growing data volumes

Those are very sobering numbers, especially considering that, “the cost of managing that data (can) exceed the value of the information!”

More and more data causes more and more problems, with the top two arguably being (1) the cost of the IT infrastructure and resources required to capture, store, and maintain that information, and (2) the inability to analyze and pull insights from the data in a reasonably simple, timely manner.

What’s the solution to coping with such immense data?  Quantivo!

Our complete utilization and exploitation of the Cloud’s incredible storage and processing capabilities allows the handling of incredibly large data sets at an incredibly low price.  It also enables our customers to realize amazing analytics performance, with super-fast answers even on complex, multi-attribute, contextual questions that would crash typical SQL-based tools.

Instead of throwing more hardware and more money at your growing mountains of data, throw the data into Quantivo’s cloud.  We can have a few billion records ready to analyze in just a few days.

Or, if data sizes aren’t a concern for you, then what about cost, time-to-answer, and access to the right answers for your business?  Even if you only have a few million records, Quantivo will give you the ability to find the patterns in your data that will directly drive your business – customer segmentation, market targeting, affinity and market basket analysis, behavioral pattern analysis, and more.

Give us a try.  We’ll make you an analytics ninja (since “guru” is sooo 2002)!

We’re so excited to have two new members added to the Quantivo team.  Both bring extensive BI and Web Analytics expertise to our already impressive team, and add considerable energy to our revolution of the behavioral analytics space.

Kim Silva is driving our West Coast sales team.  Kim brings more than ten years of technology sales experience and a demonstrated history of ensuring customer success while also driving business success.  Kim comes to us from Webtrends, and has also worked at Experian CheetahMail, and IBM Rational Software.

Jose Santa Ana is our new Senior Director of Product Marketing.  Jose brings over 20 years of experience in business applications and software, and strong international experience, having lived and worked in the Philippines, Australia, Hong Kong, and Singapore.  Jose comes to us from Omniture, and has also worked at IBM and Hyperion, and as an analyst at both Gartner and IDC.

We’re thrilled to have them both Kim and Jose aboard!

The latest issue of The Economist contains a special report on the ever-growing abundance of data being captured and analyzed across all areas of society, from business to health-care to crime.  It’s a fantastic look at how much data is available and how analysts are struggling to not only understand the implications, but to simply find ways to slice and dice immense amounts of information.

Here are a few choice tidbits that give a glimpse into the challenges facing statisticians, analysts, and more importantly, the software engineers creating the latest analytics platforms:

• Wal-Mart handles more than 1m customer transactions every hour, feeding databases estimated at more than 2.5 petabytes—the equivalent of 167 times the books in America’s Library of Congress.
• By 2013 the amount of traffic flowing over the internet annually will reach 667 exabytes, according to Cisco.
• Farecast can advise customers whether to buy an airline ticket now or wait for the price to come down by examining 225 billion flight and price records.

A few months ago, we wrote a post that mentioned some similar statistics, but the new article really blows away those numbers.

It’s going to take a really disruptive, revolutionary analytics platform to help companies digest and find the value in this kind of “big data.”  One that can analyze tens of billions of data points in just a few minutes.

I seem to recall hearing of a solution that would be perfect for this…  starts with the letter Q, I think… 

We’ve put a lot of work into Quantivo 4 and one of the highlights that we’re most proud of is our ability to dynamically segment customers based on contextually-specific behaviors.  That’s a mouthful but the potential that this brings to the market is just huge.

What context filters do for you is eliminate the need for multi-pass queries by allowing you to define the context of the query, such as items purchased in the context of a single invoice or the context of a customer’s lifetime.  Typical BI and analytics tools can tell you that a customer has purchased product A and product B, but drilling down to true context-specific answers quickly becomes difficult and time-consuming.

Let’s take a quick look at how the context impacts a real business question and dramatically changes the results of the query. The data is the same for each of these screen shots, which is point-of-sale data for a fictional home improvement retailer. In each case, we’re asking the same question: “I want to see the items (i.e. categories) purchased that contain the letters “ap” and only by customers in our “platinum” customer loyalty group, but the context of that question changes.  And remember: it takes only a single click to change the context!  And also remember:  it takes only a few seconds to answer these types of questions, even on hundreds of millions of detailed transaction or server call records!

In this first example, the context is set to “line items,” which returns line items containing the letters “ap” and purchased by platinum customers.  It’s a fairly simple question with a straight-forward answer and the only one that current BI tools can typically answer.

Read the rest of this entry »

All of us here at Quantivo are thrilled to announce the latest advancements to our award-winning and revolutionary analytics solution:  Quantivo 4!  We couldn’t be more excited to bring the world of Business Intelligence and Advanced Analytics into the the 21st Century…finally!

I could go on and on about how Quantivo 4 enhances our already powerful solution with new analytic capabilities.  I could also talk for hours about how Quantivo 4′s new, intuitive user interface enables you to simply drag-and-drop to segment your audience and customers using context-specific attributes without learning to code or write some complex querying language.  I thought about telling you how Quantivo 4 lets you immediately compare target segment results to your entire population so that you can instantly recognize opportunities and gauge their impact.  I could also fill up this post with more on Quantivo 4′s ability to analyze billions of customer clicks or transactions in just seconds.  But I should probably just get to the details, right?

I also thought about using this post to rant about how decades of BI solutions and technologies have had their chance and have failed.   But then that would lead me right into a pitch about how Quantivo has modernized analytics and introduced a new model for BI that puts answers into the hands of the people who need them, when they need them, and at a reasonable cost.  But I probably shouldn’t do that either.

Instead, let me link you to all of the details (below), and use this space to thank the entire Quantivo team for their continued hard work at developing, launching, and finalizing Quantivo 4 this quarter.  And, I’ll also thank our entire ecosystem of customers and partners for their continued support and participation in making Quantivo 4 such an exciting solution.

We are truly revolutionizing the BI and Advanced Analytics space, and this is only the beginning!

Read our Quantivo 4 press release.

Read what the press are already saying.

See Quantivo 4′s new capabilities.

Download our “What’s New in Quantivo 4″ data sheet.