How to Make Instant, Data-Driven, Business Decisions

May 18, 2018 10:45:00 AM
5 min read

Two trends are occurring in tangent: data-driven decision making and the expectation of instant automated results. Now the question is: How can we make instant, data-driven decisions?

E-commerce is an ideal area to explore this question. Online shopping inherently generates a massive amount of data to be able to make data-driven decisions, yet the timeframe to engage with an individual online shopper is relatively short. If retailers can use data to make a decision about engaging with a specific customer, within the timeframe a shopper is browsing the website, this would open up massive potential for revenue.

Data-Driven Decisions are a Competitive Edge

charts_impactIn the information age, businesses are expected to make data-driven decisions. Want to draw up a budget? Look at the data. Want to reconsider the company’s priorities for next quarter? Look at the data. Want to choose a color for an add-to-cart button? Look at the data.

Big data makes it possible to gain deep insights into consumer behavior and business performance. Going with a gut feeling or hunch is risky. A deep analysis of what was and what could be means reducing risk and maximizing potential.

The success of using data to guide business decisions is evident in the likes of Amazon, Netflix, and Google, companies that put data at the core of their processes and culture. Companies not born in the cloud are investing heavily in the tools necessary to find insights in their data. Gartner, a research and advisory firm, predicts the business intelligence market will grow from $18.3 billion in 2017 to $22.8 billion by 2020.

But as more companies adopt and mature a data-driven mindset, it will become less of a competitive edge and simply business as usual.

“Instant” is a Consumer Trend Businesses Should Adopt

As consumers, we have experienced a lot of our life become instant. Our vernacular is beginning to resemble our expectations: Instant coffee, Instacart, Instagram. Even if it’s not instant, per se, we have dramatically cut the time it takes to get what we want. New companies are making process of getting what we want as instantaneous as possible. Instant payments with Venmo, instant cabs with Lyft, instant dates with Tinder, and the list goes on.

Consumer products may be instant, but businesses still run on the slow side. Businesses are slow because of volume, caution, and complexity. Businesses have a lot of issues to deal with at once. They tend be risk averse. Any business decision has to undergo a proper bureaucratic review by all relevant parties. It takes time.

However, as data becomes the primary power of decision making, businesses may be able to adopt the consumer’s expectation of instant.

What Would Instant Data-Driven Business Decisions Look Like?

As mentioned before, e-commerce is fertile ground for the convergence of instant and data-driven decisions. When online shoppers interact with an e-commerce website, every mouse click, page view, scroll, hover, etc., is a data point that can be used to make a decision. But every shopper is only on the website for an average of just over three minutes.

To make an impact on someone shopping right now, the data-analysis and implementation must occur in an instant. The window for engagement is narrow, but the opportunity is wide open.

If a data-driven decision could be made in an instant, the e-commerce site could deliver highly relevant service based on the data the shopper is generating in real-time, at the very moment the shopper is most interested in the product, and where they have all the tools to complete the transaction. Being able to address a customer’s concerns or hesitations in real-time would increase the likelihood of conversion.

Making Instant, Data-Driven, Business Decisions Possible

There are three factors that make instant data-driven business decisions possible, regardless of industry or application.

  1. Instant Data Capture & Analysis
  2. Automated Implementation
  3. Trust in the Process

Instant Data Capture & Analysis

For instant data-driven decisions, data-capture and analysis must be done with extreme efficiency.

This is a significant barrier. Fortunately, in the last few years machine learning technology has been developed to be a cost effective and efficient solution for businesses to understand their data.

A machine learning technology like Granify can be added to an e-commerce website with a small script. Once it’s turned on, it can analyze millions of user behavioral data points at once. This means that the data capture and analysis is done at once and in real-time.

Automated Implementation

granify_decision_brainOnce machine learning technology is implemented to understand the data, a mechanism must be in place to decide what action to take and then take it.

In most cases, this process takes time. If you want to make a change to a website, someone needs to come up with a design, someone else needs to approve it, and yet another person will need to make the change. How can we speed this process up?

The answer, again, is machine learning. Machine learning technology can identify, in the same instant it analyzes data, what the optimal choice is to resolve an issue. If a shopper needs more information about a return policy, and the machine learning technology can detect this in the behavioral data and push a slight design change to get the message to the shopper. Further, machine learning technology can be used to determine what the best method of delivering information is for each individual shopper. And all of this can happen instantaneously.

Trust in the Process

As we’ve discussed, bureaucracy and complexity stand in the way of making instant data-driven decisions. Business as usual can slow down business optimization.

If machine learning technology is being used to capture, analyze, decide, and implement a business decision, the business team needs to trust the process. Otherwise, the instantaneous goal of this is null. Putting more time in between a user generating data and an action being taken only serves to weaken the impact of the action.

So how do you gain trust in some algorithm? The answer is to look at the results.

For example, Granify is run with perpetual A/B attribution split. Our partners can clearly see that revenue per session increases for the traffic affected by the machine learning technology making instant data-driven decisions.


in-store online shoppingGranify drives data-driven optimization for some of the worlds largest retailers. Request a free consultation to learn how you too can have instant data-driven optimization.

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