How are companies using their customer data?
Companies are constantly trying to boost their online sales, and by crunching number and analyzing purchase patterns they are become more adept at predicting tastes and purchase habits in online consumers. This is another big step for the marketing profession because it means that we are moving towards more technical marketing practices rather than focusing only on traditional marketing methods. While a mix is still and always will be important, it is going to be interesting to see what the future holds for marketing jobs, and how the technical aspect will be reflected in the curriculum of marketing degrees in the years to come.
Determining pricing is another way that companies are using big data. By understanding which customers are willing to pay certain prices, companies will have a better idea about what they should be charging for their product, and adapting to this understanding.
Driving customers through their funnel has and always will be a main function of marketing. Coming into the more technical side, using a CRM system to manage all of your customers and prospects will be more effective than ever. With some of the CRM software available today, understanding where your customers are in the funnel becomes so much easier. For example companies such as Hubspot offer software such as Signals which helps monitor email campaign success rates.
In addition, Prospects, which gives a marketing team the ability to track and categorize your prospects based on how deeply they have dove into your online content. Learn more about it by clicking the link.
Streamlining the user interface is another way to use big data. By analyzing where your customers are stopping along the purchase path, you are able to build hypothesis about why they are getting hung up on these areas, and why you are losing their attention. This opens the opportunity to try A/B testing in order to determine where that issue lies.
Examples of how companies are using data.
Starbucks introduced its loyalty rewards cards and has since seen 25% of their customers switch over to this method of purchasing. This is a golden opportunity for them because they are currently compiling hoards of data. So much in fact that they are puzzled at what exactly they want to do with it. What we will likely see with them is a reflection in their product offering based on what their core customers choose to purchase. You might also see the introduction of coupons catered to specified groups of consumers. For example, for customers who have been in within the last 5 months, however haven’t been in the last month, you offer them a free breakfast sandwich or 20% off coupon to get them into the store and hopefully turn them into more regular customers. These types of campaigns allow for the effective targeting of certain types of consumers and allows your company to differentiate between those who are higher in the purchase funnel, versus those who are farther along in the process. You will save money and see increases in effectiveness of your campaigns through the careful analysis and utilization of your data.
In coming years you will see a rise in the use of big data analysis to determine whether films will be blockbusters. The industry will begin analyzing things such as cast, budget, themes, genres, current events, and the use of special effects in order to determine how well movies will do. For example, if there is a trend associated with a current event, such as the election of a new president, creating movies about a presidential election would tend to fare better during those specific time periods. If George Clooney’s popularity has been on the rise, then adding him to your cast will help to drive sales. By analyzing these predictive statistics, studios will be more effective at predicting sales and weighing options for which films to make, and which films to drop.
Possibly the most interesting and successful use of big data was during the Obama campaign for presidency.
In a groundbreaking move, Obama’s campaign sent out seven unique versions of their email campaign to supporters inviting them to a $40,000 per plate dinner. The dinner took place at Sarah Jessica Parker’s home in New York (a location decided upon by the campaign’s use of data) but each of the seven emails were sent out to supporters depending on what they valued more in the experience. Emails focused on either the subjects of a second fundraiser with a Mariah Carey’s performance, and some mentioned that the editor of Vogue Magazine would be attending the dinner. His ability to effectively market to these individuals in the most relevant way possible opened up the floodgates and money began pouring in. Time’s reported an excess of $1 billion in funding which went on to finance the traditional marketing campaign and door to door efforts that won him the election.
Keep these tactics and examples in mind when considering whether to take advantage of data on your next project!
Examples of Ethically Grey Areas: Are these Practices Ethical?
Orbitz recently learned that Mac users are willing to pay up to 30% more for hotel rooms than PC users. They began showing rooms that are 30% more to Mac users because they know they can. Just to be clear, they were showing different rooms that are pricier, not charging more money for rooms and customers still had the option to categorize based on lowest price if they wished. Trends have shown that Mac users tend to be interested in more luxurious vacationing conditions and are willing to pay a higher price for them. While this initially comes off as ethically unsound, once you understand the reasoning behind it, it seems like it is more of an actual benefit to customers. Rather than focusing on price, they are actually seeing hotel rooms that are more relevant to their needs.
Another interesting example that I think is genius is Target’s use of big data to determine if a woman is pregnant. The idea came from a statistician who noticed trends in a number of woman’s’ purchasing patterns, which culminated in the purchasing of infant care items. The hypothesis was that a woman’s hormonal cycle as she enters the different stages of pregnancy can affect her purchase patterns to the point that Target in some cases could predict a woman was pregnant before there were any showing signs. Target then decided to put this to the test. They began offering coupons on pre-natal vitamins, maternity clothing, and diapers based on where big data predicted they were at in their pregnancy cycle. What were the results? IT WORKED. The coupons were being used up rapidly and the team was patting themselves on the back. Target soon found out that their fortune-teller like predictions brought in some issues. For example, a father came in yelling at the customer service representative because his 16 year old daughter had been receiving pregnancy coupons. He was in a rage and found this to be inappropriate. However, a couple months down the road he came in to apologize because his daughter was, in fact, pregnant.
The question is however, whether this constitutes an invasion of privacy into the intimate moments of their customer base. I don’t see it this way. As long as information is kept confidential, I see this more as a way for companies to provide the most relevant experience for their customers as they can. This is just the next iteration of understanding buyer behavior.