customer analytics

1月 212014

Recency, Frequency, and Monetary Analysis (or RFM) is a popular customer segmentation technique employed by database marketers everywhere. Marketers use RFM to identify which customers are most likely to respond to a direct marketing campaign. The model takes into account three simple metrics:

  • How recently did the customer buy from you?
  • How frequently does the customer buy something from you?
  • How much money does the customer spend on your products?

Each metric receives a value of 1 through 5. The result is 125 "bins" of customers (because 125 is 53). Those with higher RFM scores are considered more likely to respond to a campaign...potentially.

For years, SAS customers have used special tools like SAS Enterprise Miner to compute RFM. With SAS 9.4, the RFM algorithms are built into Base SAS, and there's an easy-to-use task in SAS Enterprise Guide. (The task is also available in the SAS Add-In for Microsoft Office.) You can find the task in the menus at Tasks->Data Mining->Recency, Frequency, and Monetary Analysis.

Note: To use the task, you must have SAS 9.4 and SAS Enterprise Guide 6.1 (or SAS Add-In for Microsoft Office 6.1). Despite the "Data Mining" category, this task does not require SAS Enterprise Miner.

As an example, suppose you have transaction data that looks like the following. You need only the 3 fields -- a customer ID, a transaction date, and a transaction amount (value):

From this, RFM calculates "scores" for each customer. The customers with the highest scores will probably be those that spent the most with you, across the most recent and frequent dates. The idea behind RFM is that a minority of customers are responsible for a majority of your business. RFM scores provide visibility into who those valuable customers are. Here's an example of the scored data, summarized at the customer level:

The RFM task supplies several useful charts. Here's a "monetization map", which summarizes the monetary values for each combination of frequency and recency scores. You might use this to help identify a "sweet spot" of customers that you want to target.

Next, let's look at a paneled bar chart of the Frequency by Recency segments. The bar on the lower right corner indicates that there are a handful of customers who made several purchases in the past (high frequency), but that was a long time ago (not recent). Perhaps that's a good target segment for a "Come back and see us -- we miss you" campaign. Contrast this with the bar on the upper right, which shows the 60 superfans: the customers who bought lately and often. You can decide whether to "go back to the well" with this group in the next campaign, or save the campaign expense as they might buy from you anyway, without prompting.

RFM scores are just one small part of planning a campaign. The "Recency, Frequency, and Monetary Analysis" task is a good start, but eventually you might want to factor in other criteria.

After all, direct marketing has many nuances, such as cross-referencing with opt-out lists and taking steps to avoid "overmarketing" to any one segment. Tracking response rates, testing campaigns, and the actual campaign workflow are also essential elements. When you're ready, SAS Customer Intelligence offers an integrated set of applications for all of these aspects.

tags: customer analytics, direct marketing, RFM, SAS 9.4, SAS Enterprise Guide
10月 242013

We as marketers always try to secure  the customer at the center of our universe.   We learned this basic truth from early years as marketers but often times we stray from that guiding principal.  We get distracted by product development, product innovation, chasing that next shiny object and the buzz we can get out of technology.  All these distractions keep us away from the customers needs and wants.

Harry & David is a leading purveyor of premium gift baskets of food & beverage.How do we stay centered with our customers?  Turn your passion centered on your customers.   Well known retailer of fruit and gourmet gifts for many years, Harry &  David has been reborn from bankruptcy with a customer centric marketing strategy built on these three keys.

1. Organize your strategic planning around a customer view: illuminate the pathway to customer success, drive balanced scorecard metrics, guide board-level /financial disclosure and force you to think from a multi year perspective.

2. Segment your customers for more meaningful relationships: drive value to the company and customers focusing on the customer conversation, the promotional strategy and resources.

3. Measure the effectiveness of marketing programs over time: focus on long-term growth via customer nurturing, measures marketing's effectiveness versus spending and lay the foundation for developing loyalty strategies.

On October 23rd, 2013, Paul Lazorisak from Harry & David was interviewed on how analytics drives a three-step loop of strategy setting, campaign execution and marketing accountability, which is accelerating the direct marketer’s customer growth at the  SAS Premier Business Leadership Conference in Orlando, Florida.  In addition, Harry & David was featured in a  conclusions paper titled "From Growing Pears to Growing Connections" going into futher detail on the marketing strategies at Harry & David.  Check out how this retailer is driving profitability through a customer centric strategy.

Thanks for following me! You can also find me on Twitter at @jfhartwig or on LinkedIn.

tags: customer analytics, customer intelligence, customer segmentation
10月 152013
Almost by definition, a customer-centric strategy demands identification of each unique customer within the customer community. Creating a representative model of the customer is a necessary prelude to developing customer profile models and analyzing any characteristics and behaviors for classification. That model must, at the very least, incorporate these aspects: [...]
10月 082013
In my last post, I suggested that there is a difference between data attributes used for unique identification and those used for attribution to facilitate customer segmentation and classification. An example of some attributes used for segmentation are those associated with location, such as home address or package delivery address. [...]
10月 012013
If a key concept of customer centricity is understanding relationship networks and any individual’s sphere of influence, it is critical that the organization of the data incorporate two different aspects of these networks. The first is the concept of a relationship, which can bind a customer to some other entity [...]
9月 202013

A table of contents lets you quickly find something in a book - often at a glance.Have you ever tried to find something quickly in a book? Say it's a book you've never read before - how do you approach it? You look first for the table of contents, right? We've all done it hundreds of times - if you scan the table of contents, you should quickly figure out what chapter or pages to turn to for what you need.

So how does that apply to marketing you might ask? Well, think of data visualization as a way to get a nice table of contents on any data set.  You can scan it and quickly draw some conclusions, and then zero in on what you need. You get good insights more quickly that let you make confident decisions at the speed of your market. It's really that simple.

Using data visualization is a way to see data graphically within a business context so you can understand it better. It can help you easily spot hidden patterns and trends, which lets you quickly figure out where you need to dig deeper. For marketers, that can mean getting more quickly to key answers, such as:

  • Finding which messages are best suited for different customers.
  • Knowing whether time of day or day of the week are more important for certain offers.
  • Figuring out how which activities have the lowest impact - so you know where to cut if you're asked.

Click on the video below for more details. In just under 2 minutes, you can see a quick story that illustrates the power of data visualization for marketers:

If you'd rather see it spelled out in a paper, download "Why Marketers Need Data Visualization" and read it at your leisure. Either way, I hope you'll quickly see what a difference data visualization can make for marketing.


tags: analytics, customer analytics, data visualization, marketing analytics
8月 202013

With the rise of mobile devices, marketers have perhaps the most robust source of customer insight and consumer access that they’ve ever had. Handled properly, they will also have the ability provide the next generation of content, coupons and services for consumers. By 2014, mobile is projected to overtake desktop Internet usage globally1, meaning the window into consumer online behavior is literally in the palm of our hands.

The opportunity for customer analytics is vast, and the volume of information generated is unprecedented. The age of big data for marketers is driven by smartphone data.

The mobile wallet race
As consumers become increasingly attached to their mobile devices, everyone from telcos, retailers, banks and payment networks (such as Visa, MasterCard and Amex) to new media providers (like Facebook, Google and Apple) are rushing to offer mobile apps that allow consumers to leave their wallets behind and make purchases directly from their phones.

Beyond simply engaging consumers at an app level, think of the customer insight marketers would have with the added insight of customer purchase patterns. The mobile device becomes the new credit card and window to the world, opening up a whole new vista of marketing and payment analytics.

Mobile: It's not just another channel
For marketing analytics practitioners accustomed to mining huge volumes of channel and campaign data, mobile could be looked at as just another channel; it's just one more component of a multichannel marketing strategy.

Marketers might even view mobile myopically as a push channel, a place to send SMS messages and offers as part of an integrated campaign. But that would just be scratching the surface of mobile's customer insight and marketing potential.

Marketing has evolved into a dynamic exchange whereby marketers capitalize on what they know about a customer based on past behavior, combined with new insights gathered through live channels in real time. Marketing has become a game of connecting content to customers. That content could be offers, but it could also be product or service information, or a myriad of other items that help create an exceptional client experience. The marketer who does that more quickly and relevantly than the competition gains share of mind – and wallet.

And that’s why mobile is not just another channel to “push” marketing offers out. The mobile device is a digitized projection of consumer interests and intent. It’s a gold mine of data for marketers looking to serve up a supreme experience – and it’s a consumer engagement platform with unprecedented power to influence, taking a 360-degree client view experience to a whole new level.

Effective mobile strategy
Consider this: Today, smartphones influence 5.1 percent of annual US retail store sales, translating into $159 billion.2 Yet by 2016 it’s predicted that influence will climb to 19 percent, or $689 billion3, according to Deloitte.

The message is clear: If you want to engage your consumer, you need a mobile strategy. And if you want to have an effective mobile strategy, you need analytics.

That journey begins with setting up the right data hooks to better understand your customers' behavior on mobile devices and mapping back to what you know about your customers via all other touch points – on and offline.

From there, you can use marketing analytics to understand and predict what content to present to customers and prospects based not only on their past behaviors and interests, but also contextually based on what they're looking for and where they're located. To do that effectively requires not only integrated marketing analytics, but also an integrated marketing system to enable that flow of content when and how the customer needs it.

The smartphone offers great potential for relevant and timely engagement, something marketers have long strived for, and consumers crave. Marketers looking to gain a competitive edge need to invest the time to capture and convert mobile data into insight that enriches the client experience. Without that, marketers will be left toiling away in traditional marketing channels that will quickly become displaced and marginalized by the power of the tiny screen sitting in the palms of our hands.

1 Source: Stanley Research.
2,3 Deloitte, The Mobile Influence Factor in Retail Sales, 2012.

Original Publication

This post originally appeared as an article in the 3rd quarter edition of sascom magazine, published by SAS. It was written by Lori C. Bieda, the Executive Lead for Customer Intelligence at SAS. As a marketing and analytics executive with 20 years of experience devoted to driving profitable business growth through the strategic use of customer intelligence, Bieda has helped Fortune 500® organizations across many sectors evolve their marketing and analytics expertise.

tags: customer analytics, marketing analytics, mobile, strategy
8月 052013

I don’t want to have a relationship with a marketing department. I don’t want to be your friend. I don’t want to engage in conversation with you. I feel no loyalty towards you. When I say I like you I’m not entirely sincere.

And yet I chose to share an enormous amount of my life with you. The detail I’m able and happy to share has grown big. Really, really big. But understand this: my reason for sharing this data is entirely motivated by self-interest. You see, I know as much about you as you do about me. I know how valuable my data can be to you. So I expect you to use this data for my benefit. Because you can be damn sure I will be.

This is the challenge we face. This is the opportunity for us to seize.

Big data is the topic of the moment in the world of marketing. As a concept, it has been very difficult to get to grips with. The term big data is nebulous. Most definitions of big data are so broad they provide no real insight into the subject. Yet there is an often overlooked truth about big data: big data is made up of lots of small data.

Specific and concrete

Our lives are full of small data. We generate it constantly as we go about our lives. In the websites we visit, location tracking on our smartphones, the time-stamp on our receipts, our Facebook updates. The jigsaw pieces of our lives that allow a marketer to better understand the individual’s personal context into which we plan to act. Small data is specific and concrete. We can understand it; where it comes from, what it says, how we can make good use of it.  I can think of no better way to illustrate the value of this specificity than with a specific and concrete example.

Importance of small data

Recently, while browsing an online article on the 10 best gaming gadgets, I found myself confronted with banner adverts for dieting, depilation and Disney. A baffling mixture of seemingly randomly selected products with no conceivable relationship to the subject matter of the page. But there is small data associated with this page that is of enormous potential value. For, as with any well-designed website, the page was loaded with key data. That metadata serves a useful purpose. It is there to help us (and Google) find the page. Eight simple phrases: wireless technology, Google Android, gaming, Apple Inc, information technology, computers, Apple Mac, and computer accessories. Eight phrases that tell you everything about the content of the page. Eight phrases that bear no relationship to dieting, depilation and Disney. Eight small, specific, concrete pieces of data that when visiting that page tell you my current interests. Not in the recent past, but right now. Eight pieces of data that marketers should use to determine if an offer for depilation is likely to be relevant to the reader.

Unlike insight derived from a person’s demographic profile or prior purchases (which tends to be relatively stable) situational context is by its very nature short-lived. So when situational context and a customer’s known preferences coincide with a product or service we wish to provide, the confluence of circumstances demands that we should act. Such opportunities may not long persist. Mid-morning coffee becomes lunchtime sandwiches; the laptop is turned off and the TV is turned on. People move on with their lives and the situation changes around them with surprising rapidity. To recognise and seize this moment when our relevance to the customer is at its highest is the key to success.

Changing the way we think

Access to these streams of small data about an individual’s ever-changing situation allows us to change the way we market. To be truly effective we may also need to change the way we think. Marketing in this environment is no longer about delivering a mass message according to the schedule we determine. It is not based on what we knew, rather it is based on what we know and it is entirely determined by the customer. Not explicitly, however. We should not sit back and wait for a call that may never come. Like a
good butler we should be invisible, observing and waiting in anticipation of the moment when we can step forward to serve, even before the recipient realises their need. Discrete, appropriate and timely. Not intrusive, overeager or too frequent. Not pretending our relationship is more than a potentially valuable adjunct to the customer’s life.

To make effective use of this small data we must understand our customers. The journey they take in deciding to make use of a product or service. Rarely simple and linear, hidden within the weave of such decisions are patterns and connections. A time, a location, a website visited, a call made, small pieces of data that in isolation may be missed, but combined act as a signal of potential intent. The challenge at the heart of big data is not then one of technology and calculation, but rather one of imagination. We need to swap our
focus from the sea of data and instead focus on the quantum of our customers’ lives and where we fit within them.

I don’t want to have a relationship with a marketing department. But I will continue to share my own personal small data with you, so that you understand when input may be useful, and equally when it is not relevant in the hope that you can serve me better.

I am happy to share this article on this blog. I originally submitted it to Marketing Week UK, which published it on May 29, 2013

tags: big data, customer analytics, customer experience analytics, customer intelligence, marketing, marketing analytics, real-time decisioning