How can marketing departments leverage big data to help their organizations increase revenue? Quite simply, by preparing sales to lead with buyer's concerns rather than waiting for the buyers to ask questions. Big data systems use new mathematical models to quickly extract and deliver usable business information from the ocean of data that’s created every second. The insights garnered from this data can help your company identify buyer concerns and structure your value propositions around those concerns.
Big data can be used to create value propositions for the buyer that benefit the bottom line. For example:
- For their very popular MMG Halo 4, Microsoft was able to process tens of thousands of real-time statistics to support an online tournament. The information gathered enabled the company to create popular Halo 4 updates that are delivered weekly and a daily email campaign to increase player retention.
- American Airlines was able to identify key social media influencers from Twitter, Facebook and Instagram to offer them one-day passes to the airline’s Admiral’s Club in an attempt to boost club membership.
- Electric utility TXU uses information gathered from smart meters to track customer usage in 15 minute increments. Knowing when customers are using power has enabled the company to offer Free Nighttime Energy between 10 p.m. and 6 a.m., as well as a discount on daytime usage. The incentive for customers to cut electric use during peak times will save the utility hundreds of millions — likely billions — in capital investments and operating costs in coming years.
CMOs from any industry can apply these same models for developing insight about the buyers’ journey to initial purchase and continuing relationships.
1. Integrate and Simplify
Though it might not sound as immediate as “geospatial analysis” or “NoSQL database,” the first step is integrating the business intelligence data you already have. If your company hasn’t tackled this integration issue, then it may be first step for the CMO to provide significant value.
Most businesses are already capturing an enormous amount of structured data from internal systems — CRM, marketing automation, data management platforms, web analytics, social analytics and content management. Bringing this information together enables you to go beyond simply reporting what happened to analyzing the “what, how and why” of business transactions; and ultimately to predicting what buyers are likely to need at the next step of their journey with your company.
The key to bringing this information together is an integrated view that simplifies the decision making process. Sometimes this can be done by implementing systems designed to integrate with one another. In other cases, it requires additional software that pulls together relevant information in order to make sense of the whole. This is sometimes accomplished through business intelligence tools, but is more and more being done with newly emerging “marketing intelligence tools” that are designed to specifically address the integration and simplification issue.
Companies addressing some of these challenges include Domo, Tableau, QlikView, GoodData and others.
2. Look Outside For Correlations and Insight
The next step is capturing unstructured information that’s outside the enterprise and integrating that with the structured data inside it. Sources include marketing intelligence systems like InsideView or social listening tools like Radian6. The good news is that many of these tools have built-in integration with CRM systems, so rather than an IT integration project the job is education and change management that helps sales quickly make these tools their own.
Tracking buyers’ and prospects’ social media posts and providing that information automatically to a sales rep when a call is scheduled enables that sales rep to better prepare to discuss the real-time pain points of your buyer.
- Let’s say the buyer tweeted a question related to an on-going business problem. Armed with this knowledge, the account manager can tailor the sales call to address the buyer’s immediate concerns — reducing the time to closing the sale. Without that information, valuable time would have been spent probing for information, and the rep would have had to follow up with a second call to address the specific business need.
A more complex example of using unstructured data comes from analysis and correlation of several seemingly independent information streams.
- Perhaps you’re tracking the trending patterns for various keywords via social listening tools. One of your key markets is health care, and you see that use of the keyword “security” grows significantly faster than in other markets. At the same time, new government regulations have gone into effect requiring new protections for patients’ medical records. Maybe the subject of medical records security and their “hack-ability” was a keynote at an IT conference. Or perhaps a well-known medical industry blogger reporting on that conference opined that hackers just hadn’t gotten interested in electronic medical records yet.
Bringing together these disparate pieces of information enables marketing to not only report that health care businesses are increasingly concerned about security, but the reasons for that heightened concern. More important, they can tailor sales content to specifically address the concerns raised at that IT conference, by that blog post or by the new regulations. In addition, you can modify your product positioning to place greater emphasis on security protections, and provide that modified product positioning to the sales organization so they can use it immediately.
- Hey Cloudera & MapR: Open Data Platform is the Real Deal
- Discussion Point: Why Do Intranets Fail?
- A Look at Gartner's Data Management Analytics Leaders
- The Sticking Point with Social Collaboration Tools
- 3 Ways Marketing Automation Boosts Business Efficiency
- Is There a Future in Content Marketing?
- 3 Vendors Lead the Wave for Big Data Predictive Analytics
- Hey Cloudera & MapR: Open Data Platform is the Real Deal