'It always surprises me that many leasing businesses understand the importance of analytics and yet they are reluctant to sufficiently invest in skilled staff and systems or expert support.'
Accessible data is key to creating a strong consumer centric leasing product, but only through advanced analytics can a business mine this information and integrate the data to turn the underlying patterns into usable insight.
Often businesses implement decisions based on relatively basic information e.g. cost drives many consumer decisions leading to cheaper product offerings. However, consumer actions are clearly not this simplistic as many other factors are part of the overall consumer decision making process such as brand perception, cost vs. utility, cost vs. value, the purchase experience, customer service / support, availability of product at the time the consumer is in the market and so on.
Given this fact, more detailed insights would provide a multi-dimensional view of the array of factors a consumer will consider in their decision and also how these interact with each other.
If we take cost as a start point, we can look at a list of items that might influence a decision to lease a vehicle:
Plus the following where not already included in a monthly rental amount:
Consumers are usually willing to trade off some of these cost attributes against each other, incurring more cost in some areas if they can offset this against other areas. For example, they might accept a higher monthly rental if it includes most or all aspects of maintenance (safeguarding them against large irregular and/or unexpected expenses).
They might focus on fuel economy (for higher mileage users), BIK tax (for those users in this 50/60yrs+ segment still in work or owning businesses), RFL Tax and Insurance costs (for those focusing on running costs other than fuel) and finally perhaps even current or future congestion charges (if frequently visiting city centres).
So how do leasing companies best review this data and understand how they should design relevant and attractive product offerings?
Having a robust analytics solution will help leasing companies analyse the data and understand how these different attributes affect the consumers decision. For different consumers these attributes will rank in a different order and they will intersect in different ways. The final result from this analysis will be a very specific answer: exactly which product suits the customer.
In the case of vehicle leasing, it’s the difference between recommending a BMW, Audi, Hyundai or a Skoda for the consumer. The results may be surprising – whereas most leasing companies may focus on manufacturers such as BMW or Mercedes in trying to get the best discounts, the best relationships may in fact lie with other manufacturers for the 50+ segment group.
Indeed some initial analysis suggests that Volvo, Skoda, Kia and Nissan could be better matches when looking at overall cost, product quality, product specification and practicality of product.
This could be taken even further. Once the consumer has made their initial selection, a recommendation engine could then serve up comparable alternatives to consumers based on price, value or eco-friendliness.
For instance, if the consumer’s initial selection is an Audi A5 Coupe, the recommendations could be:
To find out for sure however, it is essential to pull together the data you hold and deploy an advanced analytics solution to make the best possible sense of the information to drive well informed, targeted product design.
Founder and Managing Director
Blue Label Consulting
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Brendan launched Blue Label Consulting in 2011. With innovative use of Data through AI, ML and other quantitative methods, he delivers robust analytics and actionable insights to solve business problems.