Big Data allows companies to market to specific customers more so than previous attempts at targeted marketing. Marketers get excited about the readiness with which variety of data sources are available at their disposal (viz. customer call detail records, payment / prepaid top-up behaviour, online searches, social network activity, billing, etc). While Big Data Analytics allows marketers to be resourceful and creative with new ideas, backlash from angry customers is also unavoidable if they are not tactful at targeting customers / prospects.
In a recent case, the retail giant Target took the brunt of the criticism for sending out coupons to a teenage girl based on the fact that she was buying things such as unscented lotions and vitamins, things the company’s data indicated pregnant women tend to buy. It turns out she really was pregnant and she just hadn’t gotten around to telling them yet! . Efforts to build the brand perception or loyalty backfired from angry parents and embarrassed girl.
Besides being tactful about how to craft a marketing communication message that is in concert with the prospect, marketers have at their disposal new tools and techniques for doing Big Data Analytics, which they can and should leverage. For example, when segmenting customers based on age, gender, income etc. marketers are used to asking specific question against their data sources. These questions may not provide the right answer due to lack of relevant data in the database, as is often the case with Prepaid customers in the telecom industry.
Big Data Analytics provides tools for performing discovery wherein one question may lead to another that may provide relevant answers. In other words, marketers have access to tools to ask questions that may not be the one they thought of in the first place. Marketers may be able achieve better outcomes by analysing words used by different groups and correlating them to suggest age, gender etc. Phrases such as “my girlfriend / wife” is closely associated with male and “my boyfriend / husband” with females. Use of words such as “school, homework..” tends to indicate teenager; “campus, semester, assignment…” are indicative of early 20’s; “days off, office, wedding..” indicate 25 to 29 age working group according to psychological researchers.
“Marketers also need to exercise skills and tactics when selecting data sources to avoid any bias towards narrowly selecting the data”
Micro-segmentation and targeted offering may be great for laser-sharp focus on personalisation, relevance and campaign efficiencies but, may inadvertently lead to discrimination if the campaign is not exercised tactfully. For example, showing online offers only to prospects whose social network activity fit certain parameters may put the marketers at risk in the eyes of regulators who expect offers to be made available to all customers at fair and equitable terms and thus providing the choice for any customer to formally reject the offer.
Marketers also need to exercise skills and tactics when selecting data sources to avoid any bias towards narrowly selecting the data. Too narrowly choosing the target may potentially lead to missing the goal all together. Kate Crawford, researcher at Microsoft points to a review of Twitter activity to guide rescue efforts during natural disasters during Hurricane Sandy last year found that the peaks of activity occurred not in places with the most damage or need for help, like the outskirts of Queens and Staten Island, but in areas where Twitter use was most prevalent, like Manhattan. Similarly, high income earners are likely to carry smartphone and tablets compared to poorer low income and disadvantaged group.
That leads to the topic of potential problems with price discrimination. With above the line (i.e. ATL) campaigns, all prices are public knowledge. Below the line campaigns provide powerful technique for customised prices with discreet offers but may potentially lead to problems if care is not exercised. Volume discounts are public knowledge and so with student and senior citizen discounts. Where it gets into murky waters is if there is any element of unfairness when making an offer that discriminates one group from the other.
For instance, using big data analytics a marketer may accurately predict and make an offer at a higher price to a customer than offered to other customers in order to increase the margin on the sale. This is likely to create outrage from the customer, if the marketer was insensitive to the calling circle or social network of the customer who may have received the same offer at a lower price.
As availability and access to big data becomes ubiquitous, the temptation to profit from it is difficult to resist, but being tactful besides resourceful will help to avoid any pitfalls while gaining deeper insights from analytics and increased business value.
Sundara Raman is a Senior Communications Industry Consultant at Teradata. He has 30 years of experience in the telecommunications industry that spans fixed line, mobile, broadband and Pay TV sectors. He specialises in Business Value Consulting, business intelligence, Big Data and Customer Experience Management solutions for communication service providers. Connect with Sundara on Linkedin.
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