5 Ways Just About Anyone Should Be Leveraging Predictive Analytics
Sure, you're collecting a lot of data, but are you using it to predict future outcomes? Columnist David Booth discusses the ways you can apply predictive analytics to increase performance throughout your organization.
If you’re a marketer, then you know the power of data. Never before have we had more access to more data than we do right now; and for many organizations, the struggle to collect it, integrate it and store it is challenge enough. But for those who can shift their focus to using that data to enable better decisions, it is an incredible competitive advantage.
And I’m not talking about reporting here. Sure, it’s interesting to know what happened in the past, and those monthly 50-megabyte Excel files might even get read once in a while, but the organizations that are using their data to look toward and predict future outcomes are leaping ahead and uncovering enormous value.
Now, don’t stop reading just because you don’t have a team of ex-NASA Ph.D.s on staff. While there is indeed some very sophisticated work going on in the data sciences these days, predictive analytics is something that’s within reach for just about everyone.
Predictive analytics is nothing more than a way to help identify the likelihood of future outcomes based upon historical data. It can be used to optimize just about anything you can measure or define, increasing your most important measures of success.
Predictive models are quite different from descriptive ones, which can tell you what happened in the past, or diagnostic models that help explain why something has happened.
So, what are some of the applications of predictive analytics that you might be able to use right now? Well, here are five that can get you started in thinking about how you can use your data to boost performance across the different initiatives in your organization.
While we’re all chasing conversions, we also know that it’s important to understand who is converting, and this is where understanding and targeting the right prospects comes in. With the wealth of customer data you likely already have, there are quite a few things that predictive analytics can help with.
- Customer Loyalty. Predictive models can help you understand what behaviors and segments indicate a propensity to keep on consuming your products and services, as well as the behaviors and attributes that are likely to trigger a switch to another brand.
- Lifetime Value. As you’re courting prospects and evaluating your customer base, you can use your data to predict the net profit that will be attributed to the entire future relationship, and you can target your outreach, marketing campaigns, loyalty programs and more accordingly.
- Churn. Losing valuable customers is bad for business, and predicting the risk of an individual abandoning you and your brand can drive more targeted and personalized retention programs.
- Market Basket. You can use predictive analytics to understand what products are likely to be purchased together and which are likely to be purchased after one another over certain periods of time to better anticipate your buyers’ purchasing behaviors.
Predictive analytics is very often used in better allocating marketing budgets. The latest tools and techniques, coupled with the swath of data being generated on every impression and click, provide a tremendous opportunity to get the most out of every marketing dollar you’re spending.
- Marketing/Media Mix. You’re likely spending money across lots of different channels, up and down the funnel. Being able to attribute value to each touchpoint in the path to purchase and predict the budget allocation that will perform the best can get you more performance out of less spend.
Audience Targeting. The “spray and pray” targeting tactic is becoming less and less useful as we gain more and more data about who is likely to become a customer and where we can find them.
Predicting the probability of an audience to become a customer — and the value he or she may provide over a lifetime — can identify the marketing dollars that are being wasted and focus on high value prospects.
- Purchase Intent. Using behavioral and customer data to predict the intent to purchase for any lead or prospect can be enormously valuable to any organization, and this can even be modeled to predict digital’s role in driving offline sales.
Websites & Apps
Of course, if you’re investing in digital assets like websites and mobile apps, you’ll want to make sure you’re getting the most from them. Understanding what factors are likely to result in the best content, what areas can be customized to particular users and which areas of a digital experience are ripe for optimization can all be addressed through predictive analytics.
Content optimization. You’re spending time and resources to create, maintain and develop content, and you’ve likely got a lot of data around how it’s been performing.
Using that data to pull out the factors that are likely to result in success can help guide your content strategy so that you’re producing pages and experiences with a high likelihood of achieving your goals.
Personalization. When you combine digital experiences with customer data, you can start to segment and predict what groups of users are more or less likely to respond to different messages, offers, imagery and more.
With today’s personalization tools and technologies, it’s becoming more and more straightforward to achieve these user-level customizations to give people what you know they’re likely to want.
Testing Strategy. While A/B and multivariate testing is certainly not a new phenomenon, the most difficult part of testing is figuring out what to test. Predictive analytics can help you understand what areas or processes of an experience are most likely to offer room for improvement, as well as help to define hypotheses.
The testing not only can provide better and better experiences for your users, but the results can also feed the models to improve their accuracy.
Risk can be a broad category, but the reality is that organizations are attempting to reduce it in just about everything they do. Using data to understand the factors that tend to create risk and then predict when unwanted events are likely to occur can help you get a grasp of the unknown and mitigate consequences.
Fraud. For the e-commerce world, this can be a big one, and a lot of work has been done in this space.
In addition to taking advantage of larger tools and services, organizations can use their own data to evaluate the factors likely to be associated with fraudulent activity, and in many cases, proactively address it with things like additional steps in a checkout process, delayed order fulfillments or selective payment options.
Collection & Recovery. Making sure you’ve got a handle on accounts receivable has a direct impact on cash flows and an organization’s ability to operate.
Predictive analytics can help identify at-risk accounts, help decide what credit or payment terms to offer that mitigate collections risk, and even assist in defining processes that are likely to have the highest success rates.
Pricing. Putting a product out to market and maximizing value can be enormously influenced by price. Too high, and you risk acceptance and volumes; too low, and you sacrifice profitability.
Using existing product and competitive data to predict price elasticity, pricing gaps and thresholds and profitability targets can help you find the optimal price point.
It’s not just about marketing and customers: In the end, you’ll have to deliver on your products and services, and this means maximizing operational efficiencies. From predicting demand to supply chain management, predictive analytics can be an integral part of both planning and executing on operations.
Forecasting. Whether planning production runs, predicting demand for new products and services, estimating financial performance or anticipating hiring needs, historical data can be used to model probable scenarios and outcomes.
More importantly, those models can be manipulated to understand what you should be doing now to impact the results you’re likely to see in the future.
Network Optimization. Networks can mean a lot of things, including supply chains, fulfillment processes and just about anything else that has inputs, outputs and dependencies.
Leveraging the data around what factors can influence the efficiency of each node in the process can help to identify the optimal paths through them.
Now, Get Moving!
Of course, these are just a few of the areas in which organizations are leveraging the data they have available to make informed decisions about future states. And again, today’s tools, technologies and approaches are making these analyses more and more accessible to just about every organization.
So identify a business challenge, take a look at the data you have to work with, and put together a modeling solution that can help you see the future and make the decisions that will drive continual improvements.
Marketing attribution and predictive analytics: A snapshot
What it is. Marketing attribution and predictive analytics platforms are software that employ sophisticated statistical modeling and machine learning to evaluate the impact of each marketing touch a buyer encounters along a purchase journey across all channels, with the goal of helping marketers allocate future spending. Platforms with predictive analytics capabilities also use data, statistical algorithms and machine learning to predict future outcomes based on historical data and scenario building.
Why it’s hot today. Many marketers know roughly half their media spend is wasted, but few are aware of which half that is. And with tight budgets due to the economic uncertainty brought about by the COVID-19 pandemic, companies are seeking to rid themselves of waste.
Attribution challenges. Buyers are using more channels and devices in their purchase journeys than ever before. The lack of attributive modeling and analytics makes it even more difficult to help them along the way.
Marketers continuing to use traditional channels find this challenge magnified. The advent of digital privacy regulations has also led to the disappearance of third-party cookies, one of marketers’ most useful data sources.
Marketing attribution and predictive analytics platforms can help marketers tackle these challenges. They give professionals more information about their buyers and help them get a better handle on the issue of budget waste.
Opinions expressed in this article are those of the guest author and not necessarily MarTech. Staff authors are listed here.
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