Did you hear about the manager who always shot the messenger whenever they brought bad news? He eventually stopped hearing bad news. Unfortunately for him, this wasn’t because there was none to report.
Predictive analytics is being touted as the best way for marketers to unearth fresh insights into their customer base using the wealth of data, technology tools and data science skills now at their disposal.
In the recent Temkin Group report Prepare for Next Generation VoC Programs, 72 per cent of respondents cited predictive analytics models and open-ended verbatims as increasingly important sources of customer insight over the next three years. Of the large companies with more than 500 staff surveyed, 26 per cent claim to be using predictive analytics software and 36 per cent use text mining software already, with the same ratio of both actively considering investment.
But without the right approach from the CMO and business support, projects can fail to deliver the significant returns they should. So what problems can predictive analytics help the CMO to solve, how do you get started, convince your executive peers of the need for such an investment, and gather the right data to achieve the right outcome?
Big data battle
Predictive analytics is not a marketing-specific methodology or activity, but it is gaining wider application in this sector for a couple of reasons. The first is the availability and cost-effectiveness of technologies delivering the horsepower to churn through immense amounts of data. Increased levels of automation in the marketing function, along with the push to understand consumer behaviour through insights hidden within big data, are also prompting more marketing chiefs to adopt predictive analytic solutions to drive sales and efficiency.
Well-entrenched applications of predictive analytics already exist outside the marketing sphere such as fraud detection and credit risk profiling, while regular users of the methodology include law enforcement, government and pharmaceuticals. Of course like most things, predictive analytics as a concept and intention is not new, and has its roots in data mining, response modelling and statistical regression.
Eric Siegel is founder of Predictive Analytics World and author of the book Predictive Analytics:The power to predict who will click, buy, lie and die. He whittled down predictive analytics in marketing to two main goals: Who is going to buy, and who is going to cancel. The defining characteristic of predictive analytics and what separates it from forecasting is the ability to generate predictions for each individual customer or prospect.
“It’s the holy grail of marketing – to proactively pounce on every individual customer opportunity,” Siegel told CMO. “Making a prediction about each individual is not such a crazy idea. You could have a business rule for customers who fit into a certain segment based on profile, purchases of a particular product, or geography. That’s the ‘if’ part of the rule. The ‘then’ part of the rule might be that these customers are three times more likely than average to cancel their subscription.
“Whatever the business problem is you’re trying to solve, you’re applying these rules to assign a probability to an individual. It’s not about making accurate predictions, it's about predicting outcomes significantly better than guessing.”
Like most marketing innovations today, data lies at the heart of successful predictive analytics projects. All industries are getting excited about data because it represents experience and an aggregate recording of things that happened in the company or brand’s history, Siegel claimed.
“Predictive analytics is technology or methodologies that learn from that experience and works out how to predict,” he explained.
“There is a lot of excitement about big data right now, but the discussion often sidesteps the most salient question, which is: ‘What is the point of that data and where’s the value?' The most actionable thing you can get from data is predicting. These predictions directly inform the action, treatment, recommendation, contact or retention offer on a per-customer basis across millions of customers. It’s the automation of millions of decisions based on millions of predictions.”
What predictive analytics is doing for marketers
Among the applications of predictive analytics in marketing are recommendation engines for cross-selling and upselling, customer churn, and retention programs. John Elder, the founder of data mining specialist consulting group Elder Research, said text analytics is another area gaining popularity, and some companies are even starting to use link analysis to identify connections between customers and account holders.
The US-based company has customers from all industries and describes predictive analytics as a way of addressing the “needle in a haystack”.
“The meaning of predictive analytics is relatively simple: It gives me the ability to help my sales team focus on the best opportunities,” Mindjet global CMO, Jascha Kaykas-Wolff, said. “The complications and complexity of the way you deploy predictive analytics feeds into that equation, but put simply, it’s about providing clarity to the sales organisation about which opportunities they should talk to.”
The US-based company provides collaboration and project management tools and claims 83 per cent of the Fortune 100 as customers. It has already invested in employing a third-party agency to undertake several predictive analytics projects and is now looking to invest in a software solution.
Alongside the technology improvements, Kaykas-Wolff attributed the rise in predictive analytics to a change in the way companies structure their sales capability. “Sales organisations are increasingly looking at traditional enterprise sales as being inside-sales driven,” he claimed.
“The model we had in the past of expensive enterprise sales people in the field, looking for a specific buyer, doesn’t make sense anymore. The dynamic today is to have groups of people inside your company looking at opportunities opening up through your website and digital channels, who then try to mine through that data about those prospects and customers and ultimately convert them.
“When you have a lower-cost product and sales organisation, you need a way to make sure you’re filtering out the deals that will take a long time to close and bring the cost of sale to a level that suits the business.”
Read more: How predictive analytics is tackling customer attrition at American Express
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Predictive analytics in action
One of the hottest applications of predictive analytics is around customer churn or attrition. A local example is American Express’s global B2B marketing team, which is using predictive analytics to identify at-risk customers who otherwise look healthy in its database. Through a pilot project, the company was able to improve identification of attrition risks by 8.4 times, and arm their marketing and sales staff with more accurate lists of individuals to target with retention campaigns and communication.
Another high-profile and controversial international example of the insights that can be achieved through predictive analytics is from US retailer Target. The company successfully predicted one of its customers was pregnant before her father knew when using predictive analytics to apply a pregnancy prediction score to new parents-to-be.
Siegel estimated organisations using predictive analytics to solve a particular customer problem will increase response and/or decrease costs by 15-30 per cent.
“You can’t afford to give your retention offer to your entire customer base, so it has to be effectively targeted,” he said. “If your top 40 per cent of customers actually includes 80 per cent of those who will respond, then you’re going to cut your costs by 60 per cent, because you’re going to suppress the other 60 per cent of your customer list and only sacrifice 20 per cent of the sale. That makes the bottom line skyrocket.”
At Mindjet, predictive analytics is being applied to inbound trials of its software and activity inside those trials. Resulting data is then fed to the sales team to direct activity.
“The relationship between predictive analytics and the marketing and product teams is pretty important as well,” Kaykas-Wolff said. “Because we are in the SaaS [software-as-a-service] business, the relationship between those two data sources is important to use.”