How predictive analytics can impact better decision making in your organization

For over 12 years we’ve been working with executives in leading industries and enabling them to get informed decisions using business analytics. We encountered a whole slew of use cases for predictive analytics, ranging from workflow optimization, predicting churn, best offers, lead conversion, stock levels and all the way to minimize defects and improving internal HR management. 

We realized that only 4% of decision makers use predictive analytics regularly. To get a better understanding of the challenges and how predictive analytics helps business executives today we reached out to crowd wisdom. Executives from many different sectors and verticals were kind to share what they’re looking for from their predictive data analytics solution.



Executives share how predictive analytics puts them on top of their game

Shai Gonen, CFO at Intralinks, summed it up perfectly:

“Our company closes thousands of deals every year. One of our main challenges remains forecasting our revenue and growth trends and top-line results.” 

From Shai’s perspective, CFOs have to be able to predict growth trends and areas of increased potential revenue, so that their organizations business units can act on those areas and raise profits. For CFOs like Shai, data needs to enable highly accurate:

  • Revenue predictions 

  • Pipeline conversion pace

  • Predict the average selling price 

  • Predicting churn

  • Forecasting stock needs to prevent over and under stocking. 

When asked what optimizations he is after, he claimed he’d like to increase accuracy to a level of 3% and move to a more granular prediction. For example, narrowing down forecasts from a regional level to smaller geo-niches and per sales teams.

As a strong believer of predictive analytics Shai says “Companies need to create a work process that put these predictions to use. Once results will change for the better it will become an ongoing request from every business unit and manager”.

“I would recommend putting predictive analytics to use for top line results. The CFO must be the chief architect of these kind of projects – with their knowledge in these areas it can increase the probability that the organization will experience success”. 

Bottom line: predictions that affect top line results are a game changer

Eitan Saban, VP International Sales at Seismic Software, gave a similar answer, emphasizing the need

for insights into future trends and growth:

“There are many companies who’s claim to fame is sales enablement. However, Any predictive tool that could truly help me with top-line results and predict what will help me to meet my quota would be highly valuable and warmly welcomed”.

Eitan is passionate about leveraging data to optimize sales outcomes. He adds that accurately predicting trends has a strong effect on quarterly incomes, pointing out that:

“Though the availability of data about sales teams is constantly growing, once it helps us know what are the key drivers that will effect and increase our closing rates it will become a ‘must-have’”.

Constructive discussions and decision making

Bruce Stephan, Head of Customer Experience at Graco, one of the world’s premier manufacturers of fluid-handling equipment and systems, fleshed out the ways that predictive analytics can take the mass of data and add value to their understanding of the customer journey. Graco is a B2B company that both sells directly to business clients and also uses third-party partnerships. This being the case, it has many touch points where it can gather data and use it to optimize the customer experience. Bruce revealed a number of ways that predictions can assist his business:

  • Marketing: Predictive analytics helps predict the total response time of leads and their sales cycles.

  • Distribution: Predictive analytics can identify correlations between distributor type and their YoY sales performance. For example, some of Graco’s distributors sell commodity parts, while others sell full equipment systems. On this, he says, “Predictive analytics can analyze data to reveal the partners who could potentially bring us the  higher-ticket-sales through connecting data touch points that no excel sheet could.”

  • Business strategy: Predictive analytics can unify and analyze big data sets from mapping sales efforts, teams, training sessions, etc. By pointing out trends and connections we may miss out on new lines of services and businesses could be identified. 

The question we never knew how to answer – until now

As predictive analytics is still developing as a decision making tool we also encounter some interesting challenges that many of our customers face.

One chief revenue officer, who chose to remain anonymous, pointed out the challenges of working with predictive analytics. Their business is always requested to predict the number of leads that will be generated per campaign, and how many will convert each time.

 Although their data science team is building models to analyze these KPIs, they note that it takes 2-4 weeks for them to build a model, and once they do, each model only serves a specific event and has to be recreated for the next one. It’s not the speedy process that they were hoping for. And, it’s extremely costly given the expensive manpower that is needed for the task. This issue is a common refrain that we hear frequently. 

If AI is not your core business, the amount of resources you end up spending are so high it’s hard to commit to them before seeing the ROI. It’s a chicken and egg thing”. Finding solutions that are more cost effective help to put the predictive foot in the door. And that changes everything”.

Peter Chou at GumGum agrees, and also points out that even well-manipulated data may not be enough. Businesses and heads of business units to be able to communicate with data scientists. Usually misunderstandings regarding what the needed insights are on both ends generate the wrong models and waste time and resources. Also, drifting the predictive analytics penetration further away from impacting the business.  As Peter puts it:

“One of our main challenges lies in the internal communication between data science teams and our internal marketing teams. Even when data models are run, there is still work to be done to make business sense of it.”

Conclusion – Simplify the connection between business & data in your organization

We talk about the importance of ‘big data’ all the time, but for many executives, data isn’t the problem. Executives are overwhelmed with valuable data and are receiving more all the time, but they lack the tools to unlock its value in a simple, accessible, and actionable manner. Enter predictive analytics, mining data into accessible and actionable insights that power business decisions. 

Last but not least – it’s about communication first and technology later. The common theme of the need to improve communication between business and data departments and to truly make the most out of the insights that data science departments generate is still a key challenge only a few tools can help overcome.


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