Marketers are always looking for more efficient ways to drive sales and increase customer conversion, and one of the most rapidly advancing techniques they have access to is predicative analytics.
It has been around for around 40 years, but now the technology is getting more mature today and companies have access to more data than ever before.
Essentially, predictive analytics takes existing data and customer insights and uses that data to determine likely future events. With regards to marketing, companies can use it to predict customer behaviour, to better target their ads, or to better understand their customer’s needs.
[Editor’s note: This is part 1 in our two-part series on predictive analytics. Part 2, by Derek Handova, will be published on Wednesday and look at where predictive analytics fits with larger enterprise software)
Predictive analytics is coming to the forefront of many CMOs’ attention these days for a number of reasons. Mara Lederman, director of research sources and centers at the U of T Rotman School of Business, says, “Companies have data on more things than they ever have before, so that improves their ability to do predictions. It’s easier to store that data. It’s quicker to collect that data. The technology to analyze that data is becoming cheaper and more accessible.”
Lederman says that the problem many companies face with implementing a predictive analytics solution is that a lot of the time they don’t know what it is they want to predict. That’s an important place to start, and Lederman identifies three main things that business would want to predict.
The first thing is churn. If it costs a company more money to acquire a new customer that it does to keep an existing one, it should look into what it can do to keep its current customers happy so they don’t leave. Predictive analytics can be used to determine if a problem will come up, ad what solution will have the best results.
The second application is in improving how a company targets its marketing materials. It costs money to make and deliver materials like flyers and newsletters, so a company should only send those goods to customers who will actually be interested and maybe buy something.
The third thing companies might want to predict is the next best product to sell a given customer. Amazon goes this route when you order products through the website. Look below the product listing and you’ll see a section that says “people who bought this also bought” and a handful of related products will be listed. This is a predictive analytics algorithm making a guess at what other products you might want to buy.
“It’s amazing how many companies with really smart people haven’t gone through the exercise of figuring out what we would benefit from predicting,” says Lederman. “The most important thing is for companies to have a basic understanding of what predictive analytics can do so they can figure out what they want to predict and if they have the data to do it.”
There are many SaaS companies out there constantly working on new tools that can crunch millions of data points in the blink of an eye to come up with actionable insights. SAS is one such company. Monique Duquette, national practise leader of customer intelligence at SAS Canada, offers some insight on how much it has evolved and what the future might bring.
“If you look at the diversity of the data that exists today versus 40 years ago, the role of the data scientist has evolved and changed, and the talent pool is constantly being tapped,” says Duqette. “Vendors have really moved towards making predictive analytics easy by allowing you to visualize the data to determine what information you’ll need to tap into to address a business problem.”
Today, inbound marketing has become all about the “segment of one.” That means that marketers have to be able to target the individual, rather than focusing on large swaths of a demographic. Big Data, machine learning, and predictive analytics open to marketers many paths to get inside the buyer’s head and maybe even know what they want better than they do.
The role of the CMO has also changed with the increasing importance of data analysis in marketing. In the old days of Don Draper the CMOs and creative directors were primarily creative people who went with their gut instinct on things, but now the CMO has to employ a mix of creativity and analytical brain power to be effective.
For a business to fully implement the power of predictive analytics, it needs to create a culture that embraces data and what can be done with it. Businesses need to forge ahead on fact-based decision making founded on analytics, rather than shooting from the hip.
“If marketing organizations aren’t using at least some predictive analytics today, they’re lagging behind their peers,” says Duqette. “Predictive analytics really is the cornerstone of differentiation for a marketing organization.”
Read part 2 of our predictive analytics series on Wednesday, looking at how predictive analytics will integrate with larger enterprise software