James is a senior partner, based in Prophet's London office. One of his favorite brands is Chevrolet, because to convey so much emotion (Chevy runs deep) in a mass brand is pretty amazing.

Try Placing Sales (Analytics) First

salesanalyticsAnalytics 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

The Real Reason for JC Penney’s Fall

jcpenneyThe 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

Myth Busters Continued: The Foils of Big Data

bigdata2_infographicContinuing 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

big-data-infographic

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