Creating a Data-Driven Marketing Strategy
In 2014 Pep Boys Chief Marketing Officer Ron Stoupa moved to Sports Authority to take over the same role there. In a recent interview with a Forbes writer, Stoupa explains why he championed a data literacy movement: Data literacy gives marketers the power to prove their campaigns were successful. It also gives them the power to move on from unsuccessful campaigns more quickly, saving the company money. Data will be as concrete in the marketing department as profits and expenses are in finance. Marketers know that “the grey” can be an uncomfortable place. Data will bring them out of the fog and into the light, where campaign success and ROI can be proven more definitively in hard numbers.
Step 1: Hire Data Talent or Partner with a Data and Analytics Company
Data and analysis companies have been finding and scrutinizing data for decades. Sports Authority settled on Axciom, but competitors include IBM, Accenture, Cognizant and InfoSys. They’ve developed systems for onboarding new clients and it’s in their best interests to lead new clients through the entire process. You’ll work with this company to capture from data your own systems and even purchase data. Other data companies collect your consumer’s online behavioral data and provide you with the insights necessary to make well-informed marketing decisions. If your company is on board with creating a data-driven marketing team, then hiring new, experienced talent provides an alternative to bringing in outside help. Job market experts are warning, however, that the universities can’t create data professionals fast enough and demand will exceed supply for the foreseeable future. With experienced team members either from a consulting company or inside, the company can begin to collect and organize data. Understanding customers, the sales process, operations, and more requires high-level algorithms that take additional months to hammer down. This is not an overnight change, but considering how much more relevant marketing campaigns can become with data, team members should agree to the pain of growth.
Step 2: Cultivate a Data-Focused Culture
Marketers who have spent entire careers more on the creative side of customer outreach may have a tough time transitioning out of the imaginative world. Even Sports Authority’s Stoupa says that making the switch can take years. Not only do employees need to change attitudes, they will have to pick up new skills, work habits and processes.
Step 3: Use the Data to Create More Differentiated Personas
Discover the commonalities between certain types of customers and consider widening your personas from five to 10. Better-defined audiences means more effective messaging and better informed sales teams.
Step 4: Measure and Optimize
Don’t stop with the tools and the talent. The best data you’ll have access to is the results of your campaign. Build new campaigns from these results and watch your ROI rise.
What is Data Literacy and Why Is It Important?
Becoming data literate takes understanding where the data is coming from and how it’s used to sell products. Big Data has been around for decades, but it’s getting attention now because of the advent of the data collected by social and search channels. The volume of what data aggregators can now collect astounds us. Every day, people around the world post 230 million tweets on Twitter and “like” 2.7 billion posts on Facebook. These actions along with user profiles reveal each individual’s interests, shopping plans, actions, and more. Social data, however, is just one kind of data that provides insights into customer behavior and trends. Big data also comes from the equipment on the factory floor, but increasingly in homes. “Machine data” comes from the sensors and even web logs that monitor machine user behavior. Used in industrial settings until recently, this “Internet of Things” is coming to a refrigerator near you! Our everyday household devices will increasingly be embedded with sensors and network connectivity, alerting manufacturers to when parts are wearing out or supplies running low. Imagine Hewlett Packard giving you a call to say your printer ink is low and can they ship a new cartridge to you today? Machines aren’t the only data collection devices. Large companies and many B2B entities collect product IDs, prices, payment information, distributor data, and more. Called “transactional data,” businesses use this information to understand their customers and better run their supply chain.
Big Math
Collected data is worthless without the analytics applied to it that help businesses determine trends, personas, and pricing. Raw data must be analyzed in order to gain insights into customers and trends. With data the new buzz-word, however, new companies like InsightSquared, Cloudera, and Sumo Logic have joined the ranks. There will be plenty of business for math geniuses who can apply formulas to data points to determine your ideal customer’s email marketing preferences, the products they need currently, the likelihood of recommending a company, and so much more. Recently companies have used Big Data + Big Math to:
Speed up operational processesFinely tune ad targetingDetermine which ad resonates more with which audienceSpeed up customer service responsesRaise the predictive quality of business analysis reportsDeliver relevant communications to narrowly targeted audiencesDiscover new services the customer base wants
Big Data + Big Math can provide the most specific recommendations based, not on guesswork or even “best practices” (which may have nothing to do with your industry), but on concrete, data-driven insights based on your very own operations and audiences.
