Multichannel Marketing Class June 10: Measuring across channels and PPC

We talked about a number of different ways to measure responses across channels. I likened the search for the ability to measure across channels and attribute response to specific media actions to the search for the Holy Grail. There are so many new media and it is difficult to know if the response came from the last sales contact, banner ad, or search stream. Approaches vary from surveying consumers to field experiments (treating one group differently from the control to see if there is a change in sales or response rates) to building sophisticated models to simulate and measure response. I shared some research from Don Schultz at Northwestern where he has used survey data from an online panel to then determine media influence. In the example Don gave us, Auto Makers were spending too much money on television advertising and not enough on the internet, in terms of perceived influence. Industry data is useful as a starting point but many marketers want to see what the effect is on response in their particular company situation. The search will continue for the best models.

We also looked at Pay Per Click in Google and saw how expensive some keywords can be and discussed bidding strategies. It can be a good strategy to go for the third or fourth spot to save money and still be 'above the fold.'

Multichannel Marketing Class June 2: The Multichannel Database Continued

Joe Stanhope from Alterian built upon what Joe Decosmo from Allant said last night about the importance and also the difficulty of developing a database that incorporates data across functional areas and across customer touchpoints. Joe talked a little bit about Alterian, which leverages its partner relationships to help companies put all the data together and then run analytical software, campaign management software, email marketing software and web content software all using the same customer database. Joe gave an amusing example of his customer contact experiences with American Airlines. In spite of the fact that Joe is an Elite status flier and American knows a lot about him, the company still fails to make relevant offers to him using his personalized information. He has received thirty emails from the company in the last few months but offers are to where he does not fly or for benefits he does not wish to receive.

I completed our database marketing module by talking about how databases are put together. Companies typically take internal information such as customer transaction data and name and address and purchase external information, a process known as data enhancement. From this information, companies create modelled data such as RFM scores (Recency, Frequency and Monetary Value) which are usually computed in the form of deciles (ten groups), or quintiles (five groups). Customers are placed into one of the groups and marketed to accordingly. There are other ways to create modelled data and assign group scores. We talked about an alternative to RFM used by Marriott Vacation Clubs called CAP, but companies also engage in more sophisticated modelling techniques. Thus, the three types of data, modelled, internal and external, make up the basic parts of all customer databases. Types of external data might be lifestyle or psychographic data such as Claritas, PRIZM (You are where you live) which we examined in class. We also talked about how companies like Acxiom, Experian and others take data from different sources and then append that data to outside customer records to add value. These firms also use these different data sources to create their own clustering and segmentation models. Typically, a company will give an outside vendor their file to be cleaned (merge/purge, de-dup) and then records will be matched using a match code and data appended accordingly. There is a great deal of work to be done internally also to keep data clean, such as getting rid of bad records, including change of addresses, and general quality maintenance. Good data quality is a constant process. I talked a little about my research on data quality and the presentation I will make at the Marketing Science INFORMS conference on the relationship between organizational factors like a stated strategy and good teamwork and vision around customer data quality and ultimate data quality. Marketers are worried today about social media and other new marketing tools and using all channels that will be effective should be a priority for markters. However, data quality is a discipline that can reap many benefits as customer data is used across all channels.

The real multichannel marketing challenge

I attended the Direct Marketing Association Conference and presented at the associated Direct Marketing Educator's Research Summit in early October. It seems that marketing practitioners are struggling with the entire multichannel concept. The fact is, analyzing customer responses across channels to figure out the best way to communicate with them (how many touches, over what time frame and over what channel) to drive response is not an easy task. In the first place, most firms still don't have their data from various marketing programs in one place. In the second place, the level of sophistication of data analysis needed to solve these problems is considerable. Although there are software firms out solving this problem, to a large extent the marketer must rely on response models and statistical techniques unique to the firm and requiring a comfort level with statistics that most marketers do not possess. I think this trend just reinforces the fact that marketing is becoming a more technical discipline. In terms of the educational field, I think the recent students from University programs, particularly those who have taken courses like the database course I teach, will be better prepared to meet the multichannel marketing challenge.