Try Placing Sales (Analytics) First

Analytics is often seen as a resource focused on supporting marketing. However, in the era of Big Data, analytics has a great deal to offer the sales team as well. I spoke at Georgia State University last week, addressing their Sales Executive Roundtable of two dozen senior sales leaders. Here are 10 thought-starters for sale analytics that really got the debate going:

1. Create a segment of 1
Analytical tools now allow you to target an individual customer with a tailored message through an optimal channel. At Prophet, we worked with Snapfish combining both analytics and a rigorous “test and learn” approach to ensure each customer and prospect was receiving highly targeted and bespoke content. In the B2B space, we see that how customers want to be served is a key dimension of how to segment them—not via SIC codes or conventional firmographics. Their preferred method of interaction (call center, frequent rep visit, etc.) is their defining characteristic.

2. Most CLTV models are used incorrectly
How do we focus our sales resources? A CLTV model might suggest we focus our sales efforts on what look like our highest value customers (e.g. in insurance, to up-sell a home policy to an auto customer). However, CLTV models often mislead sales leaders to focus on fewer current high-value customers as opposed to a far greater number of mid-tier customers who have the potential to become higher value. …Continue reading

It’s a Trap! Insights Functions vs. Insights Systems

Too many companies fall into too common of a trap: mistaking the difference between an insights function and an insights system. Far too often, companies invest in an insights or research department, or function, only to have its value limited because they are not connected to the broader business.

And more than ever, companies are investing in primary research but getting mixed results. Don’t get me wrong. Primary research is core to being a more outside-in, customer-centric business. However, that’s just one tool – it’s what happens with those insights and who is using them that make the difference. Far too often, this primary research is conducted, summarized to a select set of stakeholders, and then put on the shelf.

An insights function is critical to bringing expertise, discipline and execution to the gathering of insights. However, what sets high-growth companies apart from their peers is having an insights system. An insights system is not bound by functional areas or business units. It is not project-based. It is not a one-way flow of information. Far too often, insights are left at the customer level, not the market level. Or even worse, they are never shared at all.

It is by definition a system. An insights system has on and off ramps for insights. A wide range of stakeholders access and contribute to the system, well beyond the insights or research function. This includes sales and marketing, innovation and R&D, service, engineering and operations, and even partners and principals. Each of these groups has key insights into customers’ needs and behaviors, competitors and ideas for growth.

So, how do you start to build out the system? Market leaders such as UPS, 3M and Microsoft have built their systems by following these proven strategic steps: …Continue reading

The Real Reason for JC Penney’s Fall

The news of JC Penney’s recent travails brings the topic of retailer pricing and promotion very much to the top of my mind. Just how can you lose almost a billion dollars in a year by assuming the consumer actually wanted every day low pricing? Many commentators have missed one crucial detail: The biggest losses actually occurred in the quarter when JC Penney had reverted to a pattern of regular and promotional pricing. It was actually unsuccessful SKU-assortment, pricing and promotion decisions that created the biggest losses, not everyday low pricing. …Continue reading

Bring Analytics out of the Basement

Analytically-driven marketing is not just about the data and the math. Successful firms of the future will be those that create an analytics culture for marketing that allows the human element to shine through. It’s as much a question of creating the right culture as it is the right data and algorithms.

I recently caught up with Dr. R. Sukumar, CEO of Optimal Strategix to discuss the future of analytics. Despite the overuse of terms such as “Big Data” being thrown around with looser and looser context and the onslaught of analytical solutions guaranteeing a solution to every marketing challenge, Dr. Sukumar sees the human element as ever-present: “There will always be a need for human interaction, particularly with new sources of data. And there will always be a need for human interpretation and application.” …Continue reading

Myth Busters Continued: The Foils of Big Data

Continuing from yesterday’s edition of Myth Busters (Big Data Edition), I now present you with myths 5-8. You can’t believe everything you hear…

Big Data Myth #5: Big data means that analysts become all-important
It is often said that Big Data will see the rise of the analysts, “the new gods of the Information Age”, as O recently heard someone call us. But the rise of the analytics team is exaggerated. The dramatic increase in data velocity means there’s no time to “brief the analytics team” now. Actually, what is required are tools to cope with velocity, volume and granularity of the data quickly. What’s really needed are analytics tools to empower marketers to do their own analyses. The intersection of technology and analytics means that Big Data is not about a shed full of analysts working away. It is all about a small group of master-analysts leveraging technology to empower marketers to do more of their own analytics and scenario-modeling and decision-support. …Continue reading

Myth Busters: Big Data Edition

Although analyzing “big data” has the power to transform your business, the ease of doing so has been over-stated. In reality, harnessing big data is still a messy and labor-intensive business. As an analytics professional, I am incredibly excited by what we can do with data, but I think some of the hype is doing us a disservice, because it creates a false expectation of how easy this work is going to be. Most things in life that are important and worthwhile are difficult, and the analysis of Big Data is no different. The solution is to take small steps, get started now with analyzing data with very specific objectives, accept that this is still very much a manual model building process, and build a staircase of successive small projects that build steadily over time into a transformative program. To begin with, don’t believe these commonly heard myths…

Big Data Myth #1: It’s Big
Big data isn’t big. And not only is “Big Data” poor English, but it’s also misleading. What we’re talking about is a large volume of data points, updated at high-frequency, with short lag to the actual event (real or near real-time). It’s very granular. It’s individual transaction data; it’s a certain credit card, paying for a certain amount of gas, at a certain gas station. Big Data is actually lots and lots of very small data. It’s not a landslide of data, it’s a sand storm. And sandstorms can blind and disorientate you. The Bedouin said a sandstorm could drive a man mad in 6 minutes. So, to help see in the storm, what other myths do we need to debunk? …Continue reading

Big Data Plus Primary Research Packs a Punch

Big Data: Wow, what an over-used expression this past year. 2012 was the year of headlines such as “Barack Obama’s Big Data won the US election,” “How Target Figured Out A Teen Girl Was Pregnant Before Her Father Did” and “Big Data knows when you’re walking past a pharmacy and texts you a coupon.” Blah, blah, blah.

The application of analytics and mining data is indeed part of a very powerful revolution, but it’s only part of the story. Analytics can tell you a lot of about the who, the what and the when – but what happened to the why? We need to combine analytics with primary research to understand more. This combination of big data analytics and primary research is the solution behind marketing analytics 2.0. …Continue reading

What Brands Inspired You in 2012?

As 2012 winds down, we’re thinking of all of the brands that have inspired us this year. BOGO brands such as TOMS and Warby Parker come to mind, as do brands that have totally reinvented themselves, such as Microsoft, Fiat and JC Penney’s.

What brands inspired you this year, and why? Let us know in the comments!

The Merits and Demerits of NPS®

For organizations seeking to create sustained business performance improvement, managing and improving customer experience is generally considered essential. Commonly, many companies track and manage customer experience using “satisfaction” surveys. Such surveys can be linked to commercial performance, and sophisticated analytics can optimize investment in customer touch-points to create improvements in prospect conversion, revenue and reduce churn. Detailed research and analytics can be specifically tailored to address business issues that create remedy plans for problems such as poor conversion at the first touch, the need for more up-sell, etc.

However, over the last decade, Net Promoter Score® (or “NPS”) has gained popularity as a simple alternative, thanks to the work of the author Fred Reichheld, Bain & Company (a consultancy), and Satmetrix (a software vendor).

NPS® is founded on a single question: “On a scale of zero to 10, where zero is extremely unlikely and 10 is extremely likely, how likely is it that you would recommend Brand X to a friend?” Respondents are then categorized into one of three groups based on their responses. Those who scored a 9 or 10 are termed “promoters,” those who gave 7 or 8 are “passives” and those scoring 6 or less are “detractors.” The percentage of respondents that fall in to each of these categories is calculated and the NPS® is found by subtracting the percentage of detractors from the percentage of promoters.

But why has this simple question been adopted so readily at the expense of more traditional satisfaction-based series? …Continue reading

Buyer Beware: Potential Perils of Dynamic Pricing

Last week, the San Antonio Spurs coach Greg Popovich made the decision to send his best three players home early after a long road trip, meaning that the Spurs played the Miami Heat without their best three players. While the game ended up being close, Popovich’s decision caused a bit of an uproar. The primary reason for that uproar is dynamic pricing. Most sports teams and venues are using dynamic pricing to extract more profit from fans. Basically, the demand to see good teams play is higher, so the tickets are more expensive. Many sports teams moved to dynamic pricing to harvest some of the profits that had previously gone to ticket scalpers. No doubt that it’s a smart pricing strategy.

But what happens when the promised value of a “good team” isn’t what you actually get? Hence the uproar and Popovich caused, and the sanctions he will receive. There is an inherent promise of value that drives the market demand.

And this isn’t the only industry where this phenomenon happens. Airlines use dynamic pricing to let demand drive the price of seats. The 5:00 p.m. flight costs more than the 10:00 p.m. flight but, as with the Spurs/Heat situation, there is no guarantee the 5:00 p.m. flight won’t be delayed and land well after the later flight. In theater, shows are often priced in part by the cast involved. There isn’t any guarantee the understudy won’t be on stage when you finally see the show, but it is highly unlikely the director would simply sit the lead actor for the Friday night show. …Continue reading