Analytics Key for E-Marketers

As a self-proclaimed data geek, I always get a kick about learning about unexploited opportunities to reap the benefits of data-mining. I salivate about the potential to make an impact using tools that haven't been used before. I truly do get excited when I see an opportunity to generate new sales simply by obtaining a better understanding of customers' wants and needs.

Take a look at this article from DM News: E-commerce should stress analytics: eTail 2008.

The article provides a recap of a recent presentation at the eTail conference in Washington. It talks about the opportunities for e-commerce firms to start to benefit from the knowledge embedded in the customer data they collect online.

He (speaker Sheldon Gilbert, founder and CEO of Proclivity Systems) said many e-commerce companies only “look at the cash register;” only what a customer is buying. To fully optimize marketing efforts, he added, companies have to dig deeper into predictive modeling, and take into account factors such as seasonal shopping cycles, consumer buying and browsing patterns, and gauging the value of certain “action words” such as the word “organic.”

Actually, some of what the speaker highlighted seemed fairly 'no-brainer' to me. Check out this point, where he talks about how to identify cross-sell opportunities:

He also said that companies should mine their databases to learn what customers of one particular product are also buying. For example, if 79% of customers that bought denim products and shoes from a retailer overlap, then the company needs to determine how to send the proper offer to those customers based on that data.

“The data will tell you what to offer,” he said. “Human behavior is fairly predictive.”

If this article is correct in stating that many e-commerce firms have not yet employed data mining tactics to improve customer strategy, than this is a huge opportunity for direct marketers. E-commerce needs analytical folks who are schooled in data analysis and modeling techniques--people who've had practical experience turning customer behavior into programs that make money.

And, guess what? As a direct marketer with a focus on analytics, I'm ready to help!

Direct Marketing Strategy: Data Mining

We talk about direct marketing strategy in just about every blog post. The reason why we do this is because we see so many marketers in a frenzied state -- just trying to get the campaigns out on time without fully thinking through how they will track and measure them on the back end. Therefore, this step never occurs, or occurs only as an afterthought. So, the value of it is diminished -- it becomes more of an exercise than a defined part of your overall strategy.

What to do about this? Well, we found an excellent article in MultiChannel Merchant today that gives you some excellent advice on how to integrate data mining into your strategy. The article, written by Rich Brough or Transcontinental Database Marketing in Toronto, provides his ideas on what he feels are "the six stages in the hierarchy of data analytics, and the value of each to a well-rounded strategic approach."

Brough emphasizes that the first thing that marketers need to employ is a consistent approach up-front to identify opportunities within the customer base. He argues that while this may take some time to put in place, the results will be well worth the effort. Therefore, he identifies these six stages for us to consider as part of building this framework:

1) Data access: This is the foundation on which marketers build by collecting all pertinent information about customers, including name, address, demographic data, history of transactions, product and service purchases, and responses to past campaigns. Every business should earmark the appropriate resources to ensure this data is as accurate and up-to-date as possible.

2) Reporting/profiling: Key performance indicators are developed and applied to track the performance of customer relationship management (CRM) initiatives over time and across customer segments. Here, marketers can also track client migrations across various segments, compare responders versus non-responders, and gauge campaign response over time.

3) Current value: The underlying premise for CRM is that not all customers provide equal value to an organization. Therefore, the first step for any CRM initiative is to measure customers by their value to the organization.

For example, 20% of clients might account for 80% of a company’s business, and would be worth a lot of the marketer’s time and money. Another 30% might be designated as moderately valuable, but having the potential to move up into the top 20%; they’d require a different kind of pitch.

The last 50% could account for just 5% of the company’s business; they are less committed, motivated largely by price, and require still another approach (or, maybe, none at all).

4) Segmentation: In this stage, marketers identify prospects who share similar characteristics – who, therefore, belong to one of several specific segments.

This provides the opportunity to focus on the highest-value segments and acquire new customers who match the segments identified as most desirable. As well, sales pitches can be custom-tailored to suit each segment using what is known about those segments. Customers can be segmented using many criteria.

But segments should focus on identifying customers with similar product and service needs as implied through neighborhood socio-demographic characteristics, life stage, usage behavior, or needs and attitudes as identified by market research.

5) Predictive analytics: Use this to predict each customer’s likelihood to initiate a particular activity in future based on their unique characteristics and past behavior.

The benefits represent a “win-win” for the organization and its customers, with marketing ROI rising, and customers receiving more relevant offers – the principle of “right message to the right customer.” Predictive models are developed to assist marketing at all stages of the customer lifecycle, including acquisition, cross-sell and up-sell, retention, and re-activation.

6) Potential value: This is assessed by combining each customer’s current value with their potential to buy more in the future. As with current value, potential value creates an even clearer way to identify the most valuable customers, the ones worth keeping.

It also helps to identify those less valuable customers with potential for entering the most-valuable category, and those low-value clients on whom it may not be necessary to spend as much.

I'm sure that you'll agree that this is excellent advice. As Brogh's states: "Using these six stages, marketers can develop a database-marketing strategic framework that differentiates customers based on the value they currently contribute to an organization, their product and service needs, and their potential future value." This is a much more strategic approach to direct marketing, and one that will have a positive impact on your ROI.

Let's face it, if we are consistently in a hurry in getting out our campaigns, we need to be as efficient as possible. This approach may take some time to set up at the beginning. However, as you move through time, your campaigns will take you less time to create, they'll be more responsive, you'll be targeting the most profitable customers, and you'll be able to demonstrate that your DM efforts are paying off -- in terms of bottom-line profitability.

Event Triggering, Data Mining, Multi-Channel Marketing -- Oh My!

We've often cited the tremendous impact that event-triggers can provide to direct marketers. In fact, we've worked with several clients to design event-triggering programs that have resulted in response rates of up to 2.1% -- much higher than utilizing traditional data sources to fuel acquisition programs.

This continues to be the case -- not only here in the US, but also in other countries. In fact, The Wise Marketer has just reported on the results of some research conducted by UK marketing firm CDMS. The firm looked at using event-triggers with an existing customer base -- and the findings were quite interesting. The first finding was a no-brainer -- that "customer marketing -- as opposed to prospect marketing -- generates a significantly higher response." I think we've all seen this -- I mean the customers that already buy from you are definitely going to be more open to reading and responding to your messaging than those cold prospects who you are trying to acquire.

More interestingly, CDMS reported (in this survey of UK-based senior marketers) that event-triggered customer marketing produced an average of 35% more responses. Now, this is something that we can all chew on! So, how did these savvy UK marketers accomplish this? "Rather than simply processing regular customer marketing campaigns in batches, some marketers have begun to use their database marketing systems to target offers based on specific, defined customer behaviour patterns."

So, with the use of data-mining, and utilizing the information that you collect on your customers, you can create highly-responsive, event-triggered direct marketing programs for your customer base. Here are some examples of the events that were utilized:

• A customer service call
  
• A type of transaction
  
• Passing preset spending levels in a particular time period
  
• A customer's birthday

In addition to utilizing these event-triggers, the survey also examined the use of integrating this concept with a multi-channel approach to further increase response rates. This also proved to be quite interesting:

Direct mail with response via post and a freephone number was gauged as producing a 20.8% uplift. Direct mail with response via post and e-mail was gauged at 20.2%, while direct mail with response via post and a personalised web link was 18.9%. Direct mail with response via post and a web site stood at 14.3%, while direct mail with response via post and telephone scored 14.1%, and direct mail with response via post and SMS (text message) scored 12.4%. In all cases, the addition of extra response media was felt to produce a significant uplift.

For us, this is some compelling data -- the kind of stuff that makes us excited! When you use intelligent direct marketing techniques hand-in-hand, the results that you can expect are truly phenomenal! As this research suggests, you can increase sales with your existing customers just by capturing some pertinent data from them as you communicate with them, then employing some very simple data-mining techniques to build a triggering program. In addition, you can build on that success -- significantly -- by using a multi-channel marketing strategy in conjunction with the triggering. The up-ticks in response are very impressive when you utilize these tools in conjunction with one another.

Just imagine what you could accomplish if you made a point of capturing your customer's communications preferences in that marketing database, and then added some slick segmentation schemes to really target the creative by segment. Wow!

This study demonstrates once again that if you really think through your marketing strategy, and then use all of the tools available, you can increase the effectiveness of every single marketing campaign.

If you have a case study that illustrates your intelligent direct marketing, please share it with us. We'd love to highlight your success and share it with all of our readers.

Customer Experience Management for the Romantically-Inclined

It's fun to see how we, as direct marketers, can impact all kinds of customer experiences. Often-times, we think of our direct marketing efforts as impacting the customer experience of car buyers, loan appliers, catalog shoppers, and the like. However, in a recent article by Earth Times, managing the customer experience for those in search of ultimate romance can be impacted by database marketing tools. Who would have thunk it?

eHarmony has recently entered into a relationship with Tealeaf (who touts itself as the leader in online customer experience management). You have probably seen the eHarmony commercials on TV that show lots of smiling, kissing, and overall happy couples who have met their true soul-mate at the relationship service website. In fact, "on an average day, more than 90 eHarmony members marry as a result of being matched on the site (Harris Interactive, 2005)."

The article goes on to state that "as eHarmony has grown, the company has sought out the most advanced technologies in order to provide an optimum user experience for its members." Hence, their speedy growth.

In fact, Tealeaf VP of Marketing, Geoff Galat, puts it all into perspective. Galat states that through their Tealeaf CX Solution, they enable eHarmony to "optimize its members' experiences, ensuring an exciting journey toward finding true love, and creating a more profitable venture for company stakeholders." So, there you have it . . . romance and profitability.

So, as a group, we direct marketers should be proud -- not only can we help our clients build more profitable customer relationships, but our techniques and theories can actually make love blossom!

Using Profitability Models with Data Mining For Direct Marketing Success

As direct marketers, we are constantly searching for better ways to reach our customers and cause a positive reaction (i.e., get them to buy some of our products or services). Recently, CIO Insight published an article that talks about utilizing profitability models in conjunction with data mining to create more profitable customer relationships (our mantra).

The authors state, "We can use these models to come up with very accurate forecasts about how long this customer will stay with us or how many purchases they'll make over the next year. So, use the basic probability model to capture the basic behavior and then bring in data mining to understand why groups of customers with different behavioral tendencies are different from each other." It's a perfect marriage of two scientific marketing tools that when joined together can really boost the success of our direct marketing efforts. It makes our hearts sing to be able to more effectively dig out those profitable customers! More importantly, this gives us the ability to enhance the customer experience by knowing how to treat our valuable customers.

In addition, using profitability modeling with data mining helps us to profile new customers and understand the likely actions of current ones. It simply makes sense to utilize these techniques together. If you're interested in this topic, make sure you read this article -- it's excellent! Also, we have written a Customer Profitability Case Study that you may enjoy.