Any company with limited resources needs to prioritise between potential customers, between potential deals. Aim to grow an existing account? Target a new customer segment? This type of questions is best answered through continuous analysis of both the existing customer base and of potential prospects, to set the right priorities. I often hear customers saying that their sales or marketing efforts aren't efficient enough, that they do not move enough leads through the sales funnel or that the sales process is too long. As a rule of thumb, a Sales team tend to activate some 45% of the leads gathered. Out of these 45%, some will be irrelevant, and some will fall through at different stages of their buyer's journey. But what if you could change this 'rule of thumb', and instead increase the conversion rate and make more leads move forward, at every stage of the funnel?
As an example, picture a Utility company in the Swedish market. If we could identify what 0.3% of companies in Sweden represent the majority, say 70%, of the energy consumption, this data would point directly at what companies this Utility company should focus sales and marketing activities. Another example: What if you knew which 7% of the industry that will represent 70% of total growth during the coming years? Then you'd be able to prioritise your business activities towards these accounts, to ensure that your business will grow with them.
The data and the analysis can tell you what companies to prioritise, but also when the time is right to approach them. In a B2B context, often characterised by long sales cycles and long contract periods, aiming right can be the difference between winning or losing market share. That's why it's so important to keep the customer data fresh and to analyse it continuously. Business is dynamic, so you'd need to apply an always-on strategy and update your priorities as companies move in and out of your focus segment.
Despite all available data these days, it's still quite common that sales organisations tend to focus interactions based on experience or gut feel. This may work short term, but if you're serious about growth it would be a mistake not to use the available data. So how do you do it?
Within the customer analysis field, there is a lot of technical development going on today. It can be hard to grasp all variants, but this is also not needed to start employing analysis to lay the ground for more efficient use of your resources. People talk about AI, predictive analysis, digital disruption, Etc. These are all interesting things, but my advice is to focus on the business problem instead.
AI and predictive analysis may sound like the way to go, but, the most urgent need could be to make a rather basic analysis of the primary business drivers. As an example, assume that 25 customers represent 30% of the turnover, while 250 customers represent 50%. What characterises the 25 accounts? What characterises the 250 accounts? Geography? Purchase pattern? Organisation? There can be many different things, so the analysis should find the common denominators that define each group and evaluate the total market potential within the group. Then the next step would be to expand the scope and find the total market potential for the group also including companies which share the same characteristics, but who are not your customers today.
To summarise, continuous analysis of the customer base should be an integrated part of every sales process. The key to success is how you set up the work, who you involve, and to be clear on what problems you are trying to solve. Then there is a true chance that your analysis will add value to your business.
" Analysing the customer base" may sound large and complex, particularly if "Artificial Intelligence" (AI) and "predictive analysis" are mentioned in the same sentence. Don't get lost in the tech and the buzz words, it doesn't have to be difficult. It's s
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