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Data-Driven Decision Making: How To Take Advantage For Business Success

By Tim Harris, VP of Strategy & Solutions, Arbela Technologies

I've been in the ERP & CRM business for over 15 years and one thing has always remained the same -- system-based decisions rely on quality data. The challenge in the past was that the complex and fixed code written was only good for a point in time.

We didn't have ML (machine learning) capabilities like we do today in the old ERP/CRM worlds. We relied on outside algorithms as "black boxes" to handle forecasting and other complex processes. But in reality we relied more on providing a lot of information to people and relying on their tribal knowledge on how to deal with that information.

The challenge in today's market is that customer preference changes easily -- forecasting still is not accurate and identifying anomalies in data is still a very manual process. This is where data driven organizations are now moving to the front of the pack in their respective industries. Let's look at a few examples of how companies are transforming with data-driven decision making.

Example - Travel & Hospitality Client

A leading travel company is leveraging an integrated CRM, marketing automation and machine learning ecosystem to:

  • Profile customers more effectively based on historical similarities of like customers using a Azure machine learning algorithm. 
  • Augment their captured data with historical data and add in outside data such as credit scores to determine the propensity and capability to book various travel packages and stack rank the leads into highest probability to close with the highest value. The bottom of the pile would receive automated brochures and other marketing material.

Once the customer is profiled and top of funnel, then direct to the appropriate call center agent who specializes in that type of travel booking and customer classification. This all happens within seconds and has netted the customer an expected 32% increase in revenue and a higher net promotor score with customers due to the focused knowledge set of the call agent vs. generic knowledge.

So how do you prioritize and determine the data that will impact your business?

  1. Determine the personas in your business you want to target (call agent)
  2. Determine the questions you want to answer (Who should I call back? How can I ensure I'm calling the best quality lead with the highest probability to close with the largest value of deal?)
  3. Define the data model required to support these answers - identify both the data you own and the external data sources that you can leverage to augment the data to reach the best qualified decision.
  4. Determine the outputs or actions from this data - who get's what lead? What do we do with the low end, low quality leads? How can we validate this process?
  5. Train, test and refine.

In today's cloud based world - this type of PoC (Proof of Concept) can be realized in a matter of weeks vs. months with large expenditure. I recommend to any of our clients to start small and build on success.

Some examples of recent discussions and implementations: 

  • Forecasting algorithm for a high volume, high mix distributor with a demand that revolves heavily around key events that they hold. Integrating marketing data, product order history and promotions into the algorithm provided a highly accurate forecast.
  • Dynamics pricing for steel manufacturing - allowing dynamics pricing based on prospects location to their site vs. competitors, current stock levels and quantities ordered. 
  • Churn analysis for SaaS software provider - augmented with telemetry data from their software which had number of logins, number of reports run, hours on the service etc. When a drop off occurred - flag them to the account rep and get them to retrain or communicate new features/capabilities before they cancel their subscription.
  • Customer profiling for retailers and manufacturers to build up fan bases for top customers and reward them across social media, email marketing, in store promotions and recognition.

Biggest Mistake I See in Data-Driven Companies

The biggest mistake I see is that companies think their data is all that they need. Augmenting your data from external or multiple data sources is the key to a well rounded profile to make your decision on. Data.com, Experian, Discovery.org, Weather or other services provide valuable information that you can augment your internal data with to provide a better decision.

The second mistake I see is trying to bite off too much up front and wanting the perfect algorithm. ML/AI is a journey not a destination, the more information you collect and analyze - the more tweaks you can make to get to that quality data-driven algorithm. Start small, build on that success and you will be far more successful.