How to grow your business without the ‘Big’ in front of ‘Data’

Many companies searching for ways to improve their top line results can be heard proclaiming: “If we would only have Big Data technology, systems or… then we would drastically be able to increase our commercial performance” and indeed, for some companies this will be true. However, for most of them this is far from reality.

Big Data to improve commercial performance is not only about having the actual Big Data or the systems to work with it, but also – and more critically – the curiosity to actually start digging into the data and derive valuable insights from it –  you then need the whereabouts to actually act upon these insights.

This blog is about how to use data to improve top line results, without the ‘Big’ in front of it. Call it ‘Medium Data’ or even ‘Small Data’. In recent years I have come across many interesting marketing and sales cases that indicate that with the right amount of curiosity and willingness to act upon insights, a lot of top line growth can be achieved. How you ask? Read on!

To keep things practical, you will find 3 stories of typical top line improvements, based on ‘medium or small’ rather than ‘Big’ data driven insights. At the end of this blog you will also find a ‘general recipe’ for arriving at these valuable insights.

An industrial maintenance service provider had recently suffered from a downturn in market demand and was seeking for ways to increase their top line. Although the company had a database of all the installed industrial equipment they could perform maintenance services on, they had never actually looked at this data in sufficient detail to see in what ways it could lead to better results.

Upon our analysis of the data, combined with the companies’ know how on industrial maintenance, we found out that the age of a certain part of the total installed base had caused the maintenance market downturn. These machines had been in operation for 20 years and they had already had their first round of large maintenance. The second was going to be in another 10 years.

Fortunately, a second part of the worldwide installed base was about to enter its major maintenance period, that could lead to improved top line results for our client. The data enabled insights into what types of machines it concerned and where these machines where geographically located. This information told the company where to send their sales people and what capabilities they were able to up- sell to their clients.

A manufacturer of building construction products used to deploy dedicated sales departments for each of their three main product lines. Due to this strategy, each geography (and even some clients) were found to be serviced by three different sales people. Their logic was that the three different products were bought by a different buyer and in a different buying process

Although limited data was available, we still discovered that this logic wasn’t valid and that in fact the buyers and the buying processes were exactly the same for these different product lines. We decided to make each sales person responsible for all product lines in a particular region and further assigned much smaller geographic regions to the sales teams

The result was that each sales person now had a much higher density of customers in a smaller region and he could sell all three product lines to almost each customer. This improved the client contact time of the sales team by an impressive 50%. This lead to significant top line growth.

A financial services provider had segmented their entire market based upon the number of cases a (potential) client could generate. They used five segments which ranged from only a few cases to a large number of cases. The obvious outcome for this market lead to heavy sales pressure towards companies with many cases and lighter sales pressure on companies with a smaller amount of cases. Their results weren’t great!

By digging carefully into their financial and client data we discovered that there were in fact three types of customers. Each customer created a different value for the company.

• The largest total value came from customers with very high numbers of cases but these cases were all small in value, leading to small revenue per case.
• The lowest total value came from customers with lesser cases, but a bit higher value per case.
• The most significant value came from customers with only a few cases (so not attractive in the old segmentation method) but a very high value per case.

Reallocating marketing and sales efforts to this last category led to an increase in new contracts signed with significant customers of 15% – this was almost 5 times as much as it was previously.

The above three examples show that even without Big Data, there is still a lot to be gained by cleverly using your ‘medium’ or ‘small’ data. A ‘generic-always-working-recipe’ is not a given, but there are some overall learnings to derive from these examples.

1. Curiosity is key

Obviously, if you do have the curiosity to start wandering around in your data, you will never find anything. Getting to the above insights and associated business improvements is a bit like gold mining. You never know what you might precisely find, but you need to be convinced that there is gold somewhere. Further, you need to have the curiosity to continually search for it. If you do not have this curious mind yourself, get someone in your team who has.

2. Build hypotheses, find bottlenecks

Second is to formulate hypotheses of what might hinder or drive your growth. The smart thing to do is to build up a funnel from total (potential) clients in the market and the factors driving their demand. Then start peeling the onion:

• how many of these know about you?
• with how many are you in actual contact with?
• how many of them are likely to become your customer?
• how many actually (may) convert and become your customers?
• how many of them are your loyal customers already?
• how many of them promote you within their networks?

After having done that, build hypotheses on what drives potential clients through this funnel and how you could influence this. Such an analysis usually shows you where the constraints in your market or commercial process are, and will lead you towards hypotheses for potential improvement actions.

3. Find relevant data

When you have formulated your hypotheses for improvement actions, go out and get the data to see if your hypotheses hold.

• For the industrial maintenance company that data was the installed base of relevant machines and their age.
• For the financial services company this data comprised of the clients, their number of cases and the value per case.
• For the manufacturer of construction products this was data describing the buyers of their different products and their buying process

All relatively small data sets, often hand crafted, but nevertheless extremely useful in leading to measures towards improved top line results.

4. Be practical

Many people working with data have a tendency to build comprehensive databases and analytics tools first, and only then start working with it. We recommend to use the lean approach. Start working with data as quickly as possible and build databases and tools along the way. This may lead to some regrets (like multiple non uniform data sets that do not fit in one system), but pace is everything here. Every day that you are not leveraging a dataset someone else may beat you to it. So, be practical. Don’t be afraid to start with multiple spreadsheets, not properly documented analysis etc…. Get it going first and optimize later.

5. Act!

Last but not least, ACT! Let’s be honest, most analyses end up untouched or un-acted-upon in a drawer. What a shame and what a waste. So, as soon as your data has led to a potential business improvement, set up a small experiment leading to tangible results (and then scale up!) or to disappointment (it didn’t work). Changing your way of working by starting an experiment is usually easier to achieve then via big large scale process improvements or organization changes. After having proved the value of a new way of working via a small scale experiment, it is much easier to scale up than to get a whole organization to change only based upon a business case or an unproven theory or concept.