Technology 5 ways to use predictive insights to get the...

5 ways to use predictive insights to get the most out of your data


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For several years now, experts have argued that data is more valuable than oil. But are companies really succeeding in extracting the most value from their data? What are some of the hidden costs of data collection and storage, and how can companies get more out of their data?

Data storms

Today, companies are confronted with an enormous amount of data. Collecting, storing and securing that data in a warehouse or data lake involves high costs. The pandemic exacerbated the problem by driving digital transformation and bringing the entire buyer journey online. That move has prompted many companies to step up their data collection efforts to make sense of a changing world.

But data in itself is not valuable. It is only valuable if you can use it to understand a changing world and take advantage of those shifts to improve the performance of your business, for example by increasing revenue growth, gaining a competitive advantage or raising the bar for operational excellence to lay.

An organization may have a pile of gold bricks, but if it doesn’t have a way to convert the gold into cash flow, that gold is essentially worthless. This is the challenge many organizations currently face when it comes to data. Many companies are sitting on a gold mine of data. But they have no way of turning it into valuable, forecast-driven insights that could support the multimillion-dollar decisions and actions that revenue teams make every day.


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By prediction-driven insights, I mean the type of algorithmically derived, probabilistic information that can help guide day-to-day actions and predict what is most likely to happen in the future — and, most importantly, have an inordinate impact on the outcome. Today, most companies analyze their marketing data by focusing on the past: what did this segment of people do in the last quarter or the same period last year? But to move from historical analysis to prediction-driven intelligence, the underlying question must be reformulated as: Which specific individuals are likely to act in the future?

Predictive insights: using data to look ahead

This shift to a predictive mindset gives a marketing person a lot to work with and plenty of potential insights. They can create a personalized offer to influence customer behavior to change course or take action sooner. They can also create lookalike audiences much more accurately, make their targeting more accurate, or expand audiences in a very strategic way by focusing on lookalikes from future high-value customers. Another option is to predict which customers are likely to churn and take action to retain them before they leave. Even a small increase in customer retention can give profits a huge boost.

Let’s say you’re a major D2C lifestyle subscription brand that spends millions of dollars a month on acquisition campaigns. You’re also likely to offer your potential new customers significant discounts on their first order, and maybe even on their second and third orders, to really engage them for the long haul. Those acquisition costs can be significant and affect margins. These types of promotions are often guided by an established heuristic or business intelligence (BI) rule.

For example, the rule may require that every VIP customer be offered a promotion. But in doing so, it extends promotions to those who would buy again without the promotion – and also misses out on offering promotions to those likely to become VIPs. This rules-based approach is expensive and ineffective. It gives discounts to customers who don’t need them, and it doesn’t build loyalty with other customers who are likely to be involved for the long haul.

Continuing with the subscription box example: Chances are, less than 20% of your subscribers are profitable, and not before they’ve ordered at least six subscription boxes. Wouldn’t you like to know who those 20% are in the first two weeks and quickly identify your “future best customers”? What about those who can become future VIPs with a little push? Finding these premium customers early can help identify similar audiences earlier in the engagement funnel.

This type of predictive intelligence and insights can be generated from the customer event and transaction data that companies already collect as part of their day-to-day operations. AI-based predictive analytics can uncover that information.

5 steps to use predictive insights

When companies want to use predictive insights to achieve significant business outcomes, they need to focus on the next steps.

Evaluate whether business intelligence rules are actually driven by the data

Does your company use predefined rules or, worse, outdated rules to make decisions? Are you tracking actual results associated with those rules and then adjusting them as needed to reflect real results? Ask yourself how your company defines a good customer and how often that customer actively interacts with your brand.

Churn can also take different definitions in specific companies. Churn can mean that a customer has completely disappeared, or it can mean that their interactions have become much less frequent. The most common definitions may not really be indicative of your business performance, but we base so much of planning, forecasting, and budgeting on those definitions.

Regularly refine your definitions of active user, good customer, and customer churn. These definitions should work for your business – even as your business, market conditions and competitive environment evolve.

Eliminate data silos

With the rapid proliferation of SaaS tools, we seem to be collecting so much more data, but most companies still struggle to integrate it properly to gain insights indicative of future performance. There are several reasons for this: internal data privacy, legacy mindset around who owns what data, delays in data warehousing strategy, or operational know-how about the mechanics of its integration.

Even within well-defined disciplines such as marketing, siloing is still a challenge that hinders performance. The CMO survey found that after a decade of integrating customer data across channels, marketers are still struggling, with most rating their organization 3.5 out of 7 on the effectiveness of their integration of customer information across purchasing, communications, and social media channels. Ironically, this score has actually fallen since 2014, with marketers saying their programs get worse over time. Creating a complete, integrated view of the customer by eliminating data silos will lead to the best decisions.

Beware of the separation of the BI and AI disciplines

When the BI team reports to the Chief Revenue Officer and an AI team reports to the CIO, it’s easy to create information silos that make it difficult to see the bigger picture. It becomes even more challenging to find actionable insights. Some companies are solving this by merging the two groups under the office of a chief data officer, but progress has been slow here, hampering results.

Don’t be too charmed by useful insights

Most analysis efforts will provide useful information that can be acted upon. But does every insight that is useful offer the same value? Absolutely not. You should focus on developing data strategies and devoting resources to getting the precise insights you need to achieve your key business objectives. This focused approach is much more efficient than rummaging through a haystack of actionable insights in the hopes that you stumble upon the one that is currently giving you just the right boost to your revenue or major efficiency gains.

Go beyond observing dashboards and reading reports

Too often, organizations are too focused on dashboards and analyzing past trends to determine future actions. Dashboards and reports are often seen as the ultimate results of data, but this thinking limits the value of data. Think about how your acquisition, revenue and retention trajectories are being orchestrated today, then feed predictive scoring data directly into those business systems and tools. This integration directly impacts your sales and profits, rather than just looking to the past.

Predictive insights: make the most of your data

Calling data the world’s most valuable resource makes sense, especially given the importance and credibility that more and more organizations place on capturing and analyzing data. But if you don’t use your data correctly, you won’t get the best results from your marketing campaigns.

Businesses need to look at how they’re using their data and identify the most valuable insights they can get from it — and then they can see what data is really useful for their goals. After all, if 87% of data science projects never make it to production, is data being used in its most valuable way?

Zohar Bronfman is co-founder and CEO at Pecan AI.

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