Improve your strategic plan through Predictive Modeling!

Predictive modeling is an excellent example where advanced analytics can be directly used to boost your company’s strategies. Whether you wish to attract new clients, promote new offerings to existing portfolios as part of your company’s cross-selling activities, retain customers or improve life-time profitability, knowing which segments to target and with what offers is key.

Case A

Optimization the cross – selling Strategy with the use of Predictive Modeling in Insurance Sector

The issue: Our client, a multinational insurance company, called ClientIQ to optimize the cross selling strategy for the bancassurance portfolio using the intelligence of customer analytics.

The actions: Combining predictive modeling techniques, with products and sales channels profitability analysis, ClientIQ analytics team identified the optimum handling cross selling strategy (“next best action”) by customer. Using the findings of the analysis, ClientIQ strategy team reallocated the cross – selling eligible universe across sales channels and products based on their ability to generate the maximum profit potential. 

The results: The new strategy proposed by ClientIQ lead to a 12% increase in the booking rate, 11% increase in the profit of new policies and 25% increase in the generated profit per sales offer.

Case B

Marketing Budget Optimization with the use of Predictive Modeling in Retail Sector

The issue: Our client, a multinational retailer, called ClientIQ to optimize the allocation of its direct marketing budget to customers with the highest profit generation ability.

The actions: Analyzing historical performance data of promotional direct marketing campaigns that have been executed by the business, ClientIQ analytics team analyzed the positive & negative responders’ characteristics, and developed predictive models to measure customers’ probability to respond positively to an offer for the strategic product areas. Using the findings of the analysis, ClientIQ strategy team, allocated then the available marketing budget for all promotional actions, to customers with the highest profit generation ability  

The results: The new strategy proposed by ClientIQ lead to a 70% increase in the response rate, 40% increase in the net revenue and 45% increase in the net margin per targeted customer.

Case C

Optimization of a Call Center Sales Strategy with the use of Propensity Models

The issue: Our client called ClientIQ to help improve the sales performance of its call center, one of its key contact points with its clientele and source of revenue. During the sales sessions, targeting of offers per customer was primarily based on the ability & experience of agents to diagnose the needs of the customer, which was far from optimum and in some cases also misaligned with the overall business strategy. Our task was to optimize targeting so as to maximize the sales potential of the channel.

The actions: ClientIQ’s teams addressed the issue holistically, employing advanced analytics with the use of Propensity Modeling to improve targeting and revisiting the sales management strategy to ensure successful implementation, sustainability and increased flexibility through close monitoring and tight sales management.

The results: Keeping capacity and monthly offers at existing levels, the new strategy, proposed by ClientIQ, lead to a 8% increase in the number of accepted offers, which in combination with the improved targeting to the most profitable ones (i.e. EBIT per offer increased by 33%), generated a net financial impact of €205,000 increased by 28%.

Case D

Pricing Optimization through Propensity Modeling

The issue: ClientIQ was called to optimize the pricing of a time-deposits portfolio, aiming to improve the deposits spread through reduction of customer rates, without putting at risk valuable customer relationships who had significant cross-sell opportunity.

The actions: Using advanced analytics and modeling techniques, ClientIQ’s strategy and analytics teams correlated customer profitability with the cross – selling probability and introduced a multi-segmentation approach and strategy.

The results: By implementing advance client management strategies, the overall deposits portfolio spread of our client increased by 80bps, generating an incremental impact to the business of €10mln per year.

Case E

Acquisitions Targeting Optimization through Propensity Modeling

The issue: ClientIQ was called to optimize – with the use of predictive modeling – the acquisitions targeting on behalf for our client. Our task was to recognize among a list of prospect customers, those with the highest probability to respond and be approved with the ultimate target to optimize the bookings vs. cost ratio.

The actions: Analyzing historical data of similar past campaigns, and combining them with demographic and transactional data of our clients portfolio, ClientIQ’s analytics team identified the statistically significant attributes of profitable leads, isolating from the contact activity leads with low profitability dynamics.

The results: Based on the readings of our analysis, our client contacted 45% of available leads, excluding the remaining 55% from any contact activity, booking ultimately 87% of the total eligible population, with a cost that was contained at the 47% of the original amount, materializing savings of €182,000 and reducing the acquisition cost per new customer by 84%.