That statement often prompts vigorous head nodding from executives, but is quickly followed by head scratching. “How can we make this happen?”
We’ve recently published a multimedia eBook Big Data, Analytics, and the Future of Marketing & Sales that attempts to answer that question. Organizations today face overwhelming amounts of data, organizational complexity, rapidly changing customer behaviors, and increased competitive pressures. New technologies as well as rapidly proliferating channels and platforms have created a massively complex environment. At the same time, the explosion in data and digital technologies has opened up an unprecedented array of insights into customer needs and behaviors.
Some companies are already turning that Big Data promise into reality. Those that use Big Data and analytics effectively show productivity rates and profitability that are 5 – 6 percent higher than those of their peers. McKinsey analysis of more than 250 engagements over five years has revealed that companies that put data at the center of the marketing and sales decisions improve their marketing return on investment (MROI) by 15 – 20 percent. That adds up to $150 – $200 billion of additional value based on global annual marketing spend of an estimated $1 trillion.
Data on its own, however, is nothing more than 1s and 0s. The companies that succeed today do three things well:
1. Use analytics to identify valuable opportunities. Successful discovery requires building a data advantage by pulling in relevant data sets from both within and outside the company. Relying on mass analysis of those data, however, is often a recipe for failure. Analytics leaders take the time to develop “destination thinking,” which is writing down in simple sentences the business problems they want to solve or questions they want answered. These need to go beyond broad goals such as “increase wallet share” and get down to a level of specificity that is meaningful.
This approach also means moving away from the “usual way of doing things.” Most sales leaders deploy resources, for example, on the basis of the current or historical performance of a given sales region. Using data to specifically unlock new opportunities requires looking at data in a new way. One chemicals company, for example, decided to look at market share within customer industry sections in specific US counties instead of looking at current sales by region, as they’d always done. The micromarket analysis revealed that although the company had 20 percent of the overall market, it had up to 60 percent in some markets but as little as 10 percent in others, including those with the fastest growing segments.
2. Start with the consumer decision journey. Today’s channel-surfing consumer is comfortable using an array of devices, tools, and technologies to fulfill a task. Understanding that decision journey is critical to identifying battlegrounds to either win new customers or keep existing ones from defecting to competitors. Some 35 percent of B2B pre-purchase activities, for example, are digital, which means B2B companies need to invest in web sites that more effectively communicate the value of their products, SEO technology to make sure potential customers are finding them, and social media monitoring to spot new sales opportunities. One online retailer, for example, tailors its offers and discounts based on predictions of how likely a valued customer is to defect.
Marketing and sales leaders need to develop complete pictures of their customers so they can create messages and products that are relevant to them. Our research shows that personalization can deliver five to eight times the ROI on marketing spend and lift sales 10 percent or more. Becoming ever more effective with this kind of targeting, we believe (and hope), will mean the death of spam.
3. Keep it fast and simple. Data worldwide is growing 40 percent per year, a rate of growth that is daunting for any marketing and sales leader. Companies need to invest in an automated “algorithmic marketing,” an approach that allows for the processing of vast amounts of data through a “self-learning” process to create better and more relevant interactions with consumers. That can include predictive statistics, machine learning, and natural language mining. These systems can track key words automatically, for example, and make updates every 15 seconds based on changing search terms used, ad costs, or customer behavior. It can make price changes on the fly across thousands of products based on customer preference, price comparisons, inventory, and predictive analysis. One bank in Latin America transformed itself from a little-known player into an institution that was ranked 11th in market capitalization worldwide in part through algorithmic marketing. It captured ATM interactions and fed next-product-to-buy algorithms to call centers, which service operators could use to make suitable offers during the customer’s next interaction.
That level of personal interaction highlights another critical point, which is that automation doesn’t mean people go away. Advanced analytics need to serve front-line staff – whether that’s a customer service operator or a sales rep in the field. To succeed companies need to shield the front line from the vast analytical complexity and deliver simple guidelines and recommended actions. One cargo airline, for example, developed a complex model that analyzed the frequently changing dynamics of the cargo industry and negotiating strategies based on supply and demand. What it delivered to its sales staff, however, was a simple “dashboard” with simple guidelines on flight capacity, corresponding pricing as well as competitor options. The result was a 20 percent boost in share of wallet.
This goldmine of data is a pivot-point moment for marketing and sales leaders. Those who are able to drive above-market growth, though, are the ones who can effectively mine that gold.