Deprecated: Function get_magic_quotes_gpc() is deprecated in /home/customer/www/ on line 453

Warning: Cannot modify header information - headers already sent by (output started at /home/customer/www/ in /home/customer/www/ on line 898

Notice: Trying to access array offset on value of type bool in /home/customer/www/ on line 717

Deprecated: Invalid characters passed for attempted conversion, these have been ignored in /home/customer/www/ on line 2850
3 Smart Data Questions for Sales and Marketing: Could? Should? How?

3 Smart Data Questions for Sales and Marketing: Could? Should? How?


What accounts could you pursue? What accounts should you pursue? How can you influence their decision? Three simple questions that will help you prioritise both your sales and marketing activities. And yet, many businesses don't pay enough attention to the three.

Prioritising sales to focus on the customers with the highest growth potential is something most businesses would like to do. In order to get there, smart analysis of the customer base is key, particularly in B2B where sales cycles can be very long and where there are many people to influence along the prospect's buyer's journey. Referring to my colleague Rickard Candell's blog on Why Analysing your Customers is the Road to Business Growth, analysing your customers doesn't have to be difficult. It's simply a matter of identifying who you want to do business with. Or rather, as a first step: What accounts could you pursue?

The 'could' translates into a look at the total addressable market to understand the full potential of your offering. An analysis of your current customer base is a key element, but also to look at industry data, company firmographics, and external behavioural data such as web behaviour. At Vendemore, we use a machine learning model to understand current customers and to detect patterns that a human eye would have a hard time detecting. The more data we can put into the model, the better the analysis and the more likely that the predictive analysis will send you the right signals. What if you actually knew which 1% of accounts that would most likely generate 70% of the total market revenue during the coming year? Running the machine learning model, the output will be a prioritized list scoring the accounts in your total addressable market. The model uses your data as well as external structured and unstructured data to ensure the highest possible predictive power. The account prioritization, i.e. the score, will show how good of a match the accounts are to your desired customers.

Which takes us to the 'should'. What accounts should you pursue? Having done your analysis, the scores will be your guide. The higher the score, the more likely that your sales and marketing efforts will pay off, and the more likely that your customers/prospects will appreciate the value of your offering. It's however important to add the human touch to the data when answering the 'should' question: Have you already approached the account? Do you have a history? Is it already a client of yours? If so, can your sales team add meat to the bone to understand where the account is in its buyer's journey?

More data equals a better opportunity to make a good analysis. But keep in mind: Shit in - Shit out. Adding data dimensions, such as an account's visits to your website, what they are reading on your site, and if they are reading about your solution or product areas on 3rd party websites, will help to build a picture of where the accounts are in their buyer's journey. What are they researching? Are they interested in your offering specifically? This type of behavioural information will add even more meat to the data analysis bone.

Taking the 'should' analysis seriously, you'd end up with a list of the accounts most likely to buy from you, as well as what products they are interested in. Maybe even more importantly, you'll get an understanding of when the time is right to approach the accounts. Quite simply, you'd know what accounts to focus your sales and marketing activities on, thereby increasing the chances of a good return on investment.

Knowing the "could" and the "should" accounts then leads you to the question "how". With your prioritized list of accounts and an understanding of the timing aspect, you'd only need to figure out how to approach them. How do you influence an account's decision process, particularly when this process can take months?

The answer lies in the collaboration between marketing and sales, and in targeted marketing activities. To influence your prospects along their entire purchase process, I'd recommend two narrowly targeted marketing activities: 1) Account-based advertising, geo-limited if that's relevant, and 2) Account-based and/or page-based retargeting. These two are both display advertising schemes with the strength to frequently remind your target accounts of your presence. This will generate brand recognition and build unconscious trust in your offering between the account's interaction with your sales team.

Now, once you've launched the targeted activities, you'll get your hands on even more data and insights to analyse. This is where the output of your efforts starts to accelerate. You can now re-visit the three questions again, with more data. Which means that basing your sales and marketing work on the could-would-how sequence of questions, you'll create a circle of information and activities that will feed itself and continuously provide you with a competitive advantage.

What accounts could you pursue? What accounts should you pursue? How can you influence their decision? Three simple questions that can lead your business to a more efficient sales process, better ROI on your marketing investments, and more loyal customers.

It's time to stop guessing, and let data lead your decisions.

Smart data. Smart decisions.

Share this entry

Vendemore Blog

See all the entries here