Data Analytics for Sustainable Growth

After months of restrictions, many businesses are trying to determine how to return to growth after battening down the hatches for so long.
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But where do you start? What is it that now makes your business vulnerable and how do you place your business on a competitive footing going forward?

While I hate the phrase ‘new normal’, it is clear that almost every business has been taking stock of where they are financially, operationally and competitively.
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But once you have an understanding of your situation and the broader market dynamics, what next?

Each business has reacted differently to changes in the market with many having limited or even stopped sales activity. And to determine how to turn the situation around you need to know what changes have occurred.

• Are there fundamental market dynamics that have changed?
• Is your product or service still required and if so, is it still required in the same quantity or form?
• Is the competition the same and are they prepared to spend?
• Are the customers you had the same customers who are coming back?

Identifying opportunities

As you look to go beyond the basics of opening up your sales and marketing operations, you should look to your Analytics teams for guidance.

​Ask them to provide the data and insight needed to expand activities profitably and to determine where new market and product opportunities are that perhaps weren’t around or strategically important before.

​Have new markets or segments opened up that you can serve with your current product offering and operational capabilities? What difficulties are the competition facing (or indeed threats to yourselves) that you can identify and react to? There may even be consolidation in the industry, which could lead to opportunities to acquire competitors’ assets to rapidly grow market share.
 
Employing machine learning techniques can reveal different segments and opportunities in your customer base and/or market place. This analysis can then be used to create more intelligently designed products and deliver them successfully to your target segments. This holds true for new customer acquisition as well as customer retention and yield management of your own customer base.
 
Can your data analytics team identify any new or underserved segments that with a little thought around your product design, you could quickly access and grow market share? As post crisis markets continue to re-baseline, the structure of products is also likely to drastically change and vehicle leasing companies need to recalibrate to this new baseline.
 
As an example, could subscription based products appeal to more consumers than before? A segmentation exercise could determine this by identifying the profile, needs and expectations of customers allowing you to create new product characteristics such as:

• Low barriers to entry – low or no deposit for customers who can afford the monthly rental but do not have or want to spend a lump sum for traditional 3 to 6 months rental in advance
• Flexible lease terms – on contract duration and/or mileage allowance as well as the option to change vehicles in-life for customers likely to have varying levels and types of use as their needs change
• Credit building – higher risk customers accepted on a monthly subscription based product to build their credit, with a view to switching to a comparable lease contract once they have built up good payment history

 
Having the data and analytics capability to identify these segments and then deriving actionable insight to capitalise on the underlying trends will enable you to target your product development and sales and marketing activity more effectively and provide you with a stronger base for growth.
 
The next part of this series will talk more specifically about Telematics opportunities.

​If you want to know how to use data analytics for sustainable growth in the current climate and beyond, please contact us here to book your free Discovery consultation. 
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Brendan Jayagopal

Founder & Managing Director. Blue Label Consulting.