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Why Analysing your Customers is the Road to Business Growth

Why Analysing your Customers is the Road to Business Growth


" 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 simply a matter of identifying who you want to do business with - it's the same as ever before.  Only the availability of information and technology has changed. And this presents an opportunity.

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?

  1. Start by asking yourself what problem you're really trying to solve?
    As analytics and AI can seem rather technical, it's easy to end up in discussions around tools and processes, driven by your operations team or by the digital analytics team. My advice would be not to focus on the technical issues, but rather to start with the business problem. Consider your situation from a sales perspective and identify your key problem(s) taking the Sales team's angle: "Too few new clients"; "To high cost of sales"; "Too low retention rate".

  2. Next question to ask: Do we have the right data to be able to solve this problem? Companies often have good data about their existing customers and interactions, but information may be missing when it comes to companies outside one's existing customer base. For current customers, there is often data available to make predictive analyses both of what they will by, when they will buy, and how much they will buy. There is a risk, though, that this list becomes too static over time and doesn't reflect new opportunities.

    I'd recommend adding an analysis of companies that are not yet your customers. Doing this, it's important to work with a partner like Bisnode who has access to relevant data to be able to identify and evaluate interesting prospects, and who has the experience to analyse data and identify customers with good growth potential.  Another challenge may be to actually use the data as basis for decisions and priorities.  Here my recommendation would be to identify the prime stakeholder in the organisation already from start and assign the responsibility to this person or unit.

  3. Give it time.
    A key to long-term growth is to make the analysis of your customer base an integrated part of the sales process and a long-term tool for the organisation. It's easy to make a good pilot from an analytics perspective, but it's often hard to see measurable business impact already at the pilot stage. One reasons behind this, is that the organisation simply is not ready to work with the results of the analysis.  High-quality data is used, the analysis delivers clear recommendations, but the organisation is not there to start working and prioritising according to the recommendations. I'd recommend starting with a pilot, but to think longer-term already from start. Limit the pilot scope, assign who should act on the results of the pilot, and start preparing for how to scale the efforts after the pilot already from start.

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.

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