Firstly, I have a confession to make, I’m a bit of a data geek. I’ve always loved the power of data to help make more informed decisions and solve problems. From using basic formulae in an Excel spreadsheet to the large-scale processing of data algorithms to drive a machine learning process, there is a huge spectrum of complexity and relative accessibility. From schoolchildren making a basic spreadsheet of temperatures and rainfall over time, taking averages for a basic future prediction to financial services companies processing millions of data-points in real-time to calculate the potential risk of an applicant so that they can choose to bid or not bid for the price comparison request in near-time, there is a dataset for everyone to use and learn from.

Over the course of the next few weeks, I’ll be sharing some of my personal and professional experience using data and, across several blog posts, I’ll look to share some of the lessons learned to help you avoid making some of the same mistakes I have. 

B2C is ahead of B2B

We’re in the era of data. Many retail organisations have a deeper understanding of each of our preferences and biases than we do ourselves. They know when to combine complementary products into an offer, re-engage the disenfranchised shopper and promote a new product line to those with the greatest propensity to purchase it. Platforms such as eBay, Amazon and Alibaba have connected suppliers and buyers of many product lines and have data at the very heart of their operation. Financial services companies are now able to query in real-time the credit and risk rating of a potential client to ensure they understand the market value (and potential cost) of doing business with them and make an offer that they hope is of representative value (or not if the risks are too high).

Whilst data is central to many of the most successful growth strategies in consumer (B2C) markets there has been a slower adoption of a data-centric approach in business-to-business (B2B) markets. 

Why is this? 

A consumer is relatively easy to track (and classify) digitally. We all tend to have data which an organisation can link together (email – social media links, credit card – person, address – people, address – demographic, etc.) and, whilst I won’t touch on the wide subject of ethics in processing personal data and privacy in this blog, many of us are prepared to compromise / share certain elements of our digital lives to have a more relevant, personalised experience on the internet.

It is easier to manage data in a consumer sense as many of our personal attributes are readily classified and, therefore, it is much easier to group segments of a consumer client base. A business could take the social profile pictures of a group of consumer customers and segment those with beards who are aged 30-55 and who live in postcodes which suggest they are a relatively high net-earner. That business could then offer a promotion to those clients who have not yet bought a high-end shaving kit and offer retargeted adverts on various social media advertising platforms.

It is also relatively easy to quantify the cost of marketing to an individual and then tracking their purchase history to ensure that the return on investment (ROI) is optimised. The company can see that they spend on average £1.99 advertising to each prospect, they converted 5% of those advertised to with a sale of the product and the lifetime value of an average client to that brand is £149. So, for every £39.80 spent on advertising that company will generate £149 over the lifetime of that client – a good and repeatable business model which could potentially be further optimised through testing the conversion rates on different advertising platforms for sub-segments of that group.

The B2B challenge

The challenge in driving data in a B2B setting is that there may be tens of thousands of employees in a company and we might not know the decision-making process of that company. Who is going to make and/or influence the buying decisions? In which region will those decision be made? How do we find the relevant employees of that business to be able to market to them? Which companies do we want to sell to? What does a good company look like for our service or product? It is a much more involved process to define the relevant attributes of an interesting prospective company and then even harder to identify the people linked to that company’s decision-making process.  

Companies are transforming many industries by building new business models which put data at the heart of their go-to-market and who realise the huge commercial opportunity for organisations which better understand their own (and their competitors) customer base.

In next week’s blog post, I’ll be exploring what are the required elements of well-structured B2B data to enable growth.