predictive-analytics-ecommerce-strategy-hero

Predictive marketing: applying it to your e-commerce strategy

Successful companies spend time and resources continually expanding their customer base. If you consider yourself a marketer, you invest time and effort educating users, prospective clients and even competitors on how your product space is changing.

Today, several new tools leverage big data, machine learning and artificial intelligence and smart usage of statistics to help you better understand your customers. Predictive marketing is an essential concept that utilizes Big Data insights to make strategic marketing decisions. Netflix, Apple, Google, and Spotify use predictive marketing to keep users engaged. It’s all based on algorithms, and those algorithms are continuously fed with data.

In the e-commerce world, predictive marketing works just like in Uber or Netflix. If behavioral data detects a pattern of intent or habit, companies can predict what you might buy next. Doing this at scale allows companies to direct limited resources at the demographics and times most likely to convert sales. With Uber, looking at the app more frequently may factor into the surge algorithm, showing increased demand even before you order, and preemptively encouraging drivers to go towards your area. With e-commerce, companies can predict what users will buy in the future and direct marketing towards those products.

By feeding historical data to the neural networks, they can tune themselves to the right answer

Machine learning is being aggressively applied in marketing to give companies an edge in prediction accuracy and lead time. Machine learning is a type of artificial intelligence, where a large number of mathematical functions imitate neurons in the brain, by continually adapting to new inputs and outputs. AI neural networks, as they are called, need to be trained. By feeding historical data to the neural networks, they can tune themselves to the right answer.

In traditional prediction and forecasting, data is mapped to the prediction by hard coding formulas, using common sense rules (or at least human-understandable math). Artificial intelligence does away with all of that. The ML model sits between your data and the prediction of the marketing actions you need to take. With ML, the model will change shape as the data changes and interprets many different data types.

Before ML was mainstream, predictive marketing was based on smaller datasets and even on focus group data. This data could be easily biased or skewed by the testing conditions, and to exacerbate matters further, it was costly to acquire. One of the most differentiating factors about predictive marketing isn’t the fact that it provides marketers actionable insights about their customers. What’s revolutionary is the scale and accuracy. This goes hand in hand with retention marketing, allowing you to keep your customers interested and invested in what you have to offer.

Big Data means data which is too large to fit or be processed by a single computer, or even the most potent workstation computers available. It needs to be managed with unique means and processed with software explicitly tuned for it. To enact a predictive marketing plan in your business, you need Predictive Analytics layers in your software to collect information about user behaviors at scale. But without sophisticated, custom algorithms to process this consumer data, it just takes up space and costs resources to store.

Deciding which question you want to ask is often the hardest part

Predictive analytics and customer data collection layers often require precise rulesets to be developed. As a marketer, you need to ask yourself what you want to predict. What are your business’s main components where you can save money, make more money or solve problems if you knew what was going to happen next? Deciding which question you want to ask is often the hardest part.

Thankfully, in the e-commerce predictive marketing world, many tools allow both marketers and data scientists to play with tools and data and to detect trends and insights.

An insight is a common term in the data science world. Typically, the data tells you something about your business’ economics. It might not be apparent at first. The data may even tell you something alarming, which goes against the grain, bucks conventional wisdom and somehow just works. These patterns are the juicy bits that data scientists long to discover because they change our perceptions about how humans work, how they behave, and how they make decisions.

More importantly, these unknown patterns and discoveries are your advantage when you find them. Many companies make decisions that go against conventional wisdom, simply because the data says so. Traditional thinking can be a powerful force holding your competitors back, but it also gives your team that much more of a leg up.

Predictive marketing techniques can parse through data that whole departments would never dream of understanding in the past, but that’s not the best part. The predictions with AI and ML are more accurate than traditional formulas. Individual buyer purchase probabilities are calculated in real-time, and marketers can use this to save a lot of money with segmenting.

Segmenting is the approach of dividing your marketing campaigns into several different groups, based on the likelihood of conversion. Some lower probabilities will have small budget ads cast their way, and high probability buyers will be subtly directed to sales. Medium level probability buyers will have the most ads and discounts directed towards them.

But, you’re not segmenting your buyers by city, demographic, or time of day. You can segment each individual by their actual behavior, and save 20% or more with the new levels of precision that ML affords. This new level of accuracy and ability to spot subtle trends is what makes ML the game-changer.

Send emails only to customers that are likely to open

When applied to older styles of marketing, like email, predictive marketing acts similarly. You can segment out your engaged prospects much more accurately and dynamically. Start by using traditional demographic data to determine customer base engagement, combining with more massive datasets of online activity. Send messages only to customers that are likely to open emails. Reduce the overall frequency and quantity of emails and reduce the risk of less interested customers unsubscribing. By precisely targeting the most engaged prospects, predictive marketing can save resources and time for the marketing team, while allowing for more digital marketing proposals to see the light of day.

In other fields like SEO and market research, predictive marketing can help with the segmentation of approaches. Your team can again save money and time while putting pressure on the most likely conversion tactics. Predictive marketing might surface unintuitive insights, leading to decisions your team wouldn’t have thought of before.

It can spur creativity and flip the paradigm. If you are using it, you might be doing something weird, something that differentiates you from the crowd. That’s a good thing, and can be rewarded by ranking higher on search engines, gathering buzz on social media or increasing conversion rates.

Predictive marketing software options

Now you’ve decided you need to see what predictive marketing is all about. Let’s talk over a few software options. Domo is the “business cloud,” operating over a wide range of business and e-commerce metrics. It provides marketers and business managers with dashboards and visuals to derive insights from customer data. You’ll need Predictive Analytics layers to procure the data. Still, once your data endpoints are available, Domo’s intuitive pipeline building blocks allow lean teams to operate at scale.

Another tool you may have heard of is Sisense. It works on Kubernetes, Linux or Docker, for cloud, on-premises or hybrid architectures. Specializing in white-label BI applications and rapid deployment, Sisense can deploy as a small team solution or a SaaS provider’s development tool.

There are entry-level apps out there as well, for teams or individuals looking to get started. Klipfolio Software makes great dashboards highlighting analytical elements at a fraction of the cost of larger enterprise suites.

Tableau has long been the market leader in Business Intelligence. With desktop and cloud options and some of the best training and certification programs in BI, you can’t go wrong. It’s recently been acquired by Salesforce, so we’re expecting great things in the near future.

And for the larger companies, Microsoft Power BI and Googleare great options. Both provide their own data collection layers, with Microsoft’s Azure Synapse connecting seamlessly with Analytics and the BI dashboards. Google provides immense information with its own analytics, TensorFlow Machine Learning software, BigQuery, and Data Studio for fast, adaptive dashboards built-in google Cloud.

Mix and match with vast training resources which companies provide for free

All of these options can integrate and provide new perspectives on your data. Mix and match the vast amount of free training resources, and soon your marketing department can benefit from being on the cutting edge. Many of the big players like Salesforce, Google, and Microsoft offer certification programs and training resources to help your team get started with Machine Learning.

In summary, employing predictive marketing in your overall e-commerce strategy is vital to keep up and get ahead of your competition and to sign new business contracts along the way. Having the right tools and using them correctly is bound to increase your chances of success.


Darya Jandossova Troncoso is a photographer, artist and writer working on her first novel and managing a digital marketing blog – MarketSplash. In her spare time, she enjoys spending time with her family, cooking, creating art and learning everything there is to know about digital marketing.