How Can Predictive Analytics Drive Marketing Success?


Making business decisions is a high-stakes affair, and in the context of stiff competition you cannot afford to opt for ineffective solutions. Sometimes you just wish there were a magic ball of sorts to tell you what’s best for your enterprise. Alas, no crystal ball can make a 100% forecast. Instead, modern technology offers a more meaningful way to gain high-probability insights into the future, and that technology is predictive analytics.

Predictive analytics is the method of interpreting historical data to single out trends and map out future outcomes, risks and opportunities. The concept of predictive analytics has been around since the early 2000s, but its practical application in the business world has only started in the last couple of years, and is now expected to be gaining momentum. According to the Research and Markets study, by 2026 the global predictive analytics market will reach $28.71 billion, which is nearly six times as much as in 2017, with the mere $5.72 billion of accounted spending.

As machine learning consultants from Itransition claim, almost any industry – from healthcare to public services – can gain benefit from embedding predictive analytics into their business processes. But for marketing, it became a real game-changer. In the sphere where keeping one step ahead of consumer trends and desires is the sought-after result, predictive analytics could not be more relevant.

This article will explore the notion of predictive analytics and dwell upon the ways and methods it can be efficiently implemented into your marketing workflow.

What Makes Predictive Analytics Work?

You are probably wondering right now – if predictive analytics emerged nearly two decades ago, why didn’t it kick off immediately? The absence of solid technology to support this data-driven process is the answer. For a long time, predictive analytics was used sporadically, powered by individually-developed tools and processes. The recent emergence and mainstreaming of big data and machine learning created a solid base for predictive analytics and made it available for enterprises all round.

Let’s tap into the modus operandi of predictive analytics and explore the roles big data and machine learning play in this intricate process.

Predictive Analytics and Big Data

When it comes to making predictions, the more data you have, the more accurate forecast you get. To yield credible results, predictive digital marketing should harness a wide range of historical data, from transactions to conversion rates, from customer surveys to social media activities. Yet the problem with big data lies in its immense scope and fragmented nature. Stored in various formats and locations, it is a jigsaw puzzle with no rhyme or reason to the human brain. This is when machine learning comes into play.

Predictive Analytics and Machine Learning

Big data would be completely useless if not for the machine learning technology that turns bytes into insights. Running on a wide range of sophisticated algorithms, the software builds predictive models that, when you input your big data, generate actionable recommendations. Machine learning algorithms must be constantly trained and updated to match the ever-shifting business environment and marketing analytics needs.

Predictive Analytics in Marketing: 4 Most Common Use Cases

Offer the right product or service

With predictive analytics, you can abandon the inefficient trial-and-error approach to new product releases and deliver goods and services guaranteed to meet your audience’s needs.

A case study. For over a decade Cisco Systems, a multinational networking equipment company, has been successfully predicting demand for its services and goods with custom propensity analytical models. Over time, Cisco grew and so did their product range and audience. To stay ahead of the curve, the company had to scale up its propensity modeling and adapt it for 170 million potential customers.

Now, their custom analytical software generates around 60,000 models, which are updated quarterly for different product categories and customer groups. And the best part of this immense and intricate system is that it requires only three to four employees to run – all thanks to machine learning automation.

Personalize customer journey

Drawing on the predictive analytics insights, you will be able to offer a tailored shopping experience to each customer and enhance your retention and brand loyalty rates.

A case study. Sephora, a global beauty products retailer, is a predictive analytics pioneer that has been implementing AI solutions to personalize promotions and marketing since the early 2000s. Recently, the company broke new ground and came up with the technology that predicts customers’ needs and wishes for skincare and make-up products. Based on one’s history of in-store and online purchases, interaction with sales assistants, and product browsing, the solution picks up the most suitable items and automatically places them into the “Recommended for You” section. The customer then can review the machine-picked products and add them to their shopping cart.

Reduce churn rate

Predictive analytics will help you identify the early signs of customer dissatisfaction and take swift action to prevent attrition.

A case study. At a certain point, a major US wireless telecom company witnessed an increase in support service calls. This could potentially lead to heavy costs and a drop in customer satisfaction levels.

Instead of mindlessly pouring money into creating more call centers, the company implemented a sophisticated customer journey analytics system. The system can predict the issue a user is most likely to face and the support channel they would use to seek help. With this knowledge on hands, the company adopted a proactive approach to customer support. This measure decreased the time customers spent in the interactive voice response unit by 67 percent and the volume of support center calls by 30 percent, saving up to $15 per call.

Augment up-selling and cross-selling

Investigating both traditional demographics data and valuable behavioral patterns, predictive analytics can deliver high-precision recommendations your customers will enjoy.

A case study. Putting predictive analytics to use, Amazon created a wholesome personalized recommendation engine that significantly boosted their sales. The system analyzes a wide scope of individuals’ historical data, such as the items a customer purchased, reviewed, rated, added to the wish list or just searched, and comes up with a highly-personalized set of similar or complementary products. For now, around 35% of Amazon sales are attributed to this sophisticated up-selling and cross-selling framework.

What to Expect When You’re Predicting

Together with exciting opportunities, predictive analytics has its share of limitations. So, if you aim to enhance your marketing strategy with it, this is what you need to be aware of:

  • The human factor is not 100% calculable

Since you deal with such a volatile and many-faceted matter as human behavior, there is a chance your predictions will not materialize, since even the biggest data set possible might not account for such variables as customers’ mood, health, or the weather outside.

  • Specialized software is a must

Processing big data scopes and calculating explicit predictions are all impossible without predictive analytics software. Luckily, the market of off-the-shelf solutions has been burgeoning in the last couple of years, offering a decent selection to suit any taste and budget. Also, in case no software suits your business environment, you can always resort to custom development.

Are You in for Leveling Up Your Marketing?

Predictive analytics, the sophisticated concept pioneered by top-of-the-line enterprises, is becoming a common business practice before our eyes. Marketing deals with vast volumes of miscellaneous data by nature, and predictive analytics can dispel the uncertainty strongly associated with building marketing strategies based on consumer trends.

Bear in mind though, that implementing predictive analytics in practice is not a walk in the park. The model you build or have custom-developed will call for dedication, effort, and spending in return for high-precision predictions. Still, isn’t it a fair price for all the benefits?

Author Bio:

Dmitry Azarov is Chief Marketing Officer at Itransition, Denver-based software development company.