What Is “Big Data”
“There were 5 exabytes of information created from the dawn of civilization till 2003, but that much information is now created every two days” Eric Schmidt Executive Chairman of Google.
That increase in data and our ability to store and analyze it has created a new science that empowers medicine, industry and even marketing. The term often refers simply to the use of predictive analytics to extract value from data. Accuracy in big data may lead to more confident decision making. And better decisions can mean greater operational efficiency, cost reduction and reduced risk.
Why is There So Much Data: “Three V’s”
The convergence of three factors are exponentially expanding the data being generated. Big data sets are so large and complex that traditional data processing applications are inadequate. Thus the emergence of this new data science and the field of predictive analytics.
Where Does Big Data Come From
Big Data Enables Predictive Analytics
Big data analytics is the process of examining large data sets containing a variety of data types to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. [Wikipedia]
Predictive Analytics Enables Micro-Targeting
- Predictive analytics strengthen competitive advantage by generating unique and relevant prospect insight, the ability to predict very specific behaviors and the knowledge to know where to find prospects in uncluttered environments
- All of this allows us to develop more detailed Personas that enable more precise product development/offering, personalized benefit structures and better messaging
How Target Rich Solutions Has Used Big Data Analytics
Fourth Largest Rent A Car Brand: Business Program
This case study highlights the ability of Big Data Predictive Analytics to prevent adverse selection and micro-target the right customer prospect segment.
Client was a private equity funded start-up acquired from the number two largest rent a car organization in an FTC- mandated auction. When acquired, it had been used as a price leader and almost all volume came through the least profitable channel, online travel agencies like Expedia and Priceline. As a result, it had almost no repeat business; only 4% rented more than once.
Service initiatives were put in place that provided the amenities frequent renters [mostly business] required, such as expedited/express lines, automated billing and a better fleet. But in order to compete for the business traveler, even more was needed.
Client developed a business renter’s program that was unique to the Industry. It offered flat rates nationwide or the best rate available in every market with no blackout dates. The challenge was to acquire business accounts that would rent primarily Monday through Friday in major markets and to avoid the cost of acquiring businesses/renters who were loyal to one of the major brands. Furthermore, Client needed to avoid attracting price sensitive infrequent leisure travelers who might sign up but only use the firm once or twice to take advantage of the price and best customer benefits.
The firm utilized Big Data Predictive Analytics to build a behavioral profile of the desired business/renter and where they could be found online. This model was optimized over a three-month period testing everything from ad creative and landing pages to travel timeframe and time of day.
- Target: Selectively identify and acquire a very unique business traveler for a frequent traveler B2B program
- Target: Exclude typical price conscious leisure travelers. Adverse selection was important to prevent price shoppers from finding and using the program for one-off travel
- Target: Find Travelers who rent 6 + times a year, are unaffiliated with any competitor’s programs, who are value “biased” buyers, who rent mostly for business and are high value future prospects
- Using Big Data and predictive analytics we optimized display ads and followed these targeted prospects around the Web to their most frequently visited websites where they were served display ads
- The result: The firm acquired and activated 11K accounts in three months at an initial cost of sale of less than 10% [compared to an average of 20% through the OTA channel]
- Once acquired these accounts required no further investment which drove down cost of sale on each subsequent transaction
Leading Outdoor Sporting Equipment Brand: Targeted Retail Promotion
This case study highlights the incredible accuracy of Big Data Predictive Analytics in terms of behavior, location, real time, retail shopping, etc.
Client makes the world’s best hunting arrow [also winner of Olympic Gold Medals] and an award winning line of wild game and Venison seasonings and preparation accessories.
In 2015 there were 14.8 million paid hunting licenses in the US [6% of the US adult population]. Many hunter’s process some or all of their own harvest. The market for venison food processing is by far the largest. Venison seasonings and accessories are a highly seasonal category and are primarily purchased during a six-week period coinciding with deer hunting season in each state.
Given the competitiveness of big box and specialty retailers, our client needed to deplete its entire retail inventory during that timeframe or be faced with the return of unsold merchandise which would then have to be destroyed under food safety laws. To confound matters, the product was only available in 800 stores, all located in C and D Counties.
The challenge was how to effectively reach deer hunters in-season, during the purchase period who shopped at these stores for this category of merchandise.
Predictive analytics were used to profile these individuals and identify those who had a current hunting license. Then drive them to a geo-specific retailer to purchase.
- Big Data was used to identify/define the core group of serious hunters, then profiles were created that matched behavior such as category purchase/choice, credit card purchases at target retailers, geo specific retail locations, what websites they frequent and the exact date of the season in their area/state
- We then reached them online at frequently visited websites with display ads, driving them to a customized landing page where they were incented to shop in a store near them during a defined timeframe
- The program not only helped our client sell out, it generated an additional 30% in sales volume