Marketing is one of the departments whose effective use of business intelligence and analytics can have a direct impact on the organization’s top line revenue, a tier one-performance measure.

One activity from the marketing department is to run marketing campaigns to generate sales leads. The higher quality the lead the more likely the prospect is to buy the organization’s product. The more the product fits the prospect’s needs, wishes, and desires, the more likely the prospect is to be satisfied with the product after the purchase. The more satisfied the prospect-turned-customer is with the product, the more likely he or she is to buy other products from the company and/or to post a positive review on one a retail rating or social media site.

One strategy for maximizing this product sale life cycle is to move from mass marketing to target marketing to personalized marketing. Mass marketing sends the same marketing campaign to everyone or to a group. Target marketing customizes the offer to fit the characteristics that would trigger action from a specific group. Personalized marketing customizes the offer to characteristics specific to a single person.

Stephen Baker in his book The Numerati gave a great analogy for visualizing how marketing to an individual might be triggered. Imagine, you are sitting in a coffee shop watching the person next to you for hours. Your neighbor is searching the internet and visits amazon with the search string ‘manga art’. She looks at the descriptions of a couple of books and reads the reviews from an author called ‘Irene Flores’. She does a Google search using the author’s name, and visits her Facebook page and blog. She likes Irene’s Facebook page and adds post that she looks forward to seeing her Anime Expo conference. Then, she goes back to amazon and searches for ‘manga art’ again. She goes through the process for two more manga authors -amazon, internet search, Facebook like. She buys an airline ticket and books a hotel and car rental at the location of the Anime Expo conference. Then she opens up Sketchbook Pro on her laptop.

Now, we know a lot about your neighbor and we can make some assumptions about her interest and consequently her buying patterns. The facts we know is that she will travel to the conference in three months time. We can infer that she is an artist and has some type of interest in manga artist. Therefore, if we offered products that have something to do with manga, art, travel, we could target her with tailored offer. This kind of data is available today. Many organizations use this type of model for tailoring marketing campaigns. In fact, it is similar to the recommendation algorithms used by Google ads and amazon.

Using business intelligence and analytics, we can combine data from different sources, analyze it, and use the results to make personalized recommendations on what to offer. Combining historical purchase data, web usage data, and social media data is becoming commonplace in data warehouse environments. Segmenting buying patterns into groups, and profiling and characterizing past purchases can lead to recommendation algorithms that can target people at an individual level. Privacy invasion is carefully avoided; the analyst and the technology does not know (or care) who the person is by name or as an individual.

In short, BI and analytics can assist marketing to target prospects more accurately, resulting in increased campaign response rates, revenue, and uplift; campaign uplift is the difference in response rate between a treated group and a random control group. According to the Aberdeen study Towards Segments One by Trip Kucera and David White, the top 35% of respondents in their survey reported campaign response rates of 8.9%, campaign uplift rates of 7.7%, and customer retention rates of 77%. The top performing were defined based upon the best response rates and year on year improvements in sales transactions and customer retention rates. Table 1 is a comparison between the leaders and the remainder of respondents in the study.  To reach these performance levels, the leaders use predictive analytics, have marketing staff dedicated to discovering customer insights, observe and analyze customer behavior, monitor and use different marketing channels, and use data from a variety internal and external source for their analysis.

n=79	Leaders (Top 35%)	Followers (Bottom 65%)
Campaign Response Rates	8.9%	3.4%
Campaign Uplift Rates	7.7%	4.0%
Retention Rates	77%	60%
Source: Aberdeen, April 2013

Table 1 Leader vs- Follower Benefits to Individual Marketing

In conclusion, BI and analytics can contribute to increased revenues by supporting individual marketing or a ‘Segment of One’ as referred to by Aberdeen.

Reference

Baker, Stephen. Numerati. New York: Houghton Mifflin Company, 2008.

White, David, and Trip Kucera. “Towards Segments of One: Predictive Analytics of Marketing Delivers the Future of Offer Management Today.” Analyst Report, Abeerdeen Group, April 2013.