The signs of a thriving business are easily measured: growing revenue and expanding profit margins. In this post, well consider how data management helps increase revenue and turn to profit in our next post.
Investors often look at the revenue line first when evaluating a company, since a steady increase in sales is a good indicator of its overall health. Flat or declining sales is an alarm bell that suggests the company has lost its way, or has limited appeal, meaning it could produce near-term profit but wont make the long haul.
For brokerages that want to be acquired, need to raise funds or, as a public company, must keep their shareholders happy, the message is clear: the market loves growth and hates contraction.
Brokerages have a well-defined sales process for reviewing plans, priorities, and progress, but effective data management adds to that process and increases the chance of success. It does so by making sure youre targeting the right customers with the best offers.
Target the right customers
Many brokers are tempted to pitch for every possible sales opportunity, but a shotgun approach often stretches the business in too many directions. Were not saying you shouldnt look for new business, far from it, but it pays to focus your efforts.
A better approach is to exploit your greatest asset your customer base and build from there.
Use lifetime value to focus on your best customers
Not all customers are equal. Some might have been acquired in the early stages of your business, but youve outgrown their budgets. Others are just plain difficult to deal with and more trouble than theyre worth.
Calculating Customer Lifetime Value (CLV) the value of the cash flows you expect to generate from a customer over the length of your relationship helps decide where you should focus your resources. It identifies under-performing accounts, prioritizes customers for retention, cross or upsell opportunities and, ultimately, delivers a higher return from the sales force.
Modern brokerage platforms make it straightforward to calculate CLV.
Customer intelligence, drawn from your data, provides an integrated view of all transactions for each customer and itemizes income and costs, meaning you can answer questions like:
- Which clients are profitable, break even, or unprofitable for the business?
- What is the clients claims history relative to policy income?
- What is the total income a client brings to the firm vs. the number of staff hours and resources the client requires on an ongoing basis?
- How much time do you spend on your best customers vs. those who are high-maintenance?
CLV considers past and future cash flow and, while extracting historical information is straightforward, predicting the future obviously isnt.
However, analytical tools can model data and produce different scenarios to help with forecasting a more valuable input than intuition or guesswork.
Target the right new business
Knowledge of your customer base can also help you decide on the best place to prospect for new business.
Data analysis can extract insights such as the most profitable line of business and characteristics size, geography, industry of high CLV customers. By mapping those against a marketing database of potential targets, you can extract a weighted prospect list to focus sales and marketing campaigns.
Intelligent selling
Having identified the right customers, the next step is to optimize your sales approach.
Maximize your share of customer spend
Increasing your share of customer spend is a well-worn sales objective for most brokers, and data mining makes it much easier.
For example, a query such as what customers do we have in the construction industry who employ over 500 people and who buy contractors all risk but not product & casualty or directors & officers produces a list of customers who can be targeted with P&C or D&O products.
This data-led approach is more likely to produce a result than a generic mailshot or unstructured probing by the sales team.
Additionally, analysis of purchasing histories, service requests, and marketing inquiries can be used as prompts by the sales team, allowing them to make relevant and timely product recommendations.
Knowledge-based prospecting
Understanding the buying behavior of a customer with a similar business profile will ensure you contact a prospect at the right point in their buying cycle. And reviewing any previous contact theyve made with the brokerage marketing inquiries for example will help make your approach more relevant.
Use data-based insight for better sales management
Most sales leaders focus on deals near closing, but thats too late to exert influence and change the buying criteria in your favor. Fortunately, analytics can easily identify opportunities at different stages, select those that merit attention and display the results on easy to view dashboards.
Moreover, reminders can be added to high priority opportunities, meaning leaders and producers are alerted when a deadline is near.
Conclusion
Revenue growth is a tell-tale sign of a brokerage that’s doing well. But in an effort to keep sales high, its easy to waste resource on low-value customers or ill-conceived sales campaigns. Data management increases the chance of success by making sure youre targeting the right customers with the best offers.