“We must move from numbers keeping score to numbers that drive better actions.”
Spoken by David Walmsley, Head of Multichannel at Marks & Spencer, that quote cuts directly into the spirit and purpose of business data analytics. By aggregating and analyzing the numbers, data and other information that your business generates on a daily basis, you can gain a deeper understanding of what is and isn’t moving you closer to your growth goals.
In other words, data can be used to ask better questions. But contrary to popular belief, data cannot give you concrete answers without context like high-level market knowledge, expert insight and your instincts as a leader.
The Value of a Data-Informed (Not Data-Driven) Business Strategy
“Data should help inform your decisions,” says Guido Bartolacci, New Breed’s Head of Demand Generation Marketing, “but it cannot make those decisions for you.”
Why? “Data is always a look into the past,” says Guido. “It’s never going to tell you what to do, but it can tell you how things are performing.”
For example, data might tell you that your organic traffic has dipped significantly over the past month, but it doesn’t necessarily tell you why your traffic dipped at that time. Did Google update its algorithm? Did you change your SEO strategy recently? Did something occur in the greater market that has rendered your current content strategy obsolete?
The answers to those questions need to come from you, not a computer.
All that said, business leaders can and should use data as a tool for developing creative solutions, optimizing their processes and, ultimately, steering their companies in the right direction.
5 Ways to Leverage Data Analytics to Grow Your Business
1. Demand Generation
“One of the primary functions of demand generation is to have complete coverage over your sales and marketing funnel,” says Guido. “Marketing usually ends around the marketing qualified lead stage, and then all contact with that lead transitions to sales — but there’s no individual team or team member who takes ownership across the entire funnel.”
As a demand generation marketer, you can use data analytics to bridge that gap and understand how your entire funnel is performing. Using data analytics to check in with your performance on a regular basis can help you understand exactly where you need to focus your efforts in the future.
“For example, when I went over New Breed’s performance report for 2018, every area of our funnel was pretty solid in comparison with our goals and previous year’s performance,” says Guido, “except for our MQL to SQL conversion rate.”
Noticing a below-benchmark MQL-SQL conversion rate in your funnel is an opportunity to revisit your marketing-to-sales handoff process. Look at your system integrations, your call metrics and other elements of the handoff process to get a sense of why an unusual number of leads are dropping off during that stage.
“And that’s exactly what New Breed did,” says Guido.
By using data analytics to peer into the inner workings of our funnel, we were able to form a hypothesis as to why our MQL-SQL conversion rate was underperforming and take steps to remedy that.
2. Conversion Rate Optimization
Conversion rate optimization is another key area of business performance where data analytics can shed some light.
“The first two things to look at as far as conversion rate optimization is concerned are your landing pages and the conversion opportunities on your website,” says Guido.
Because landing pages aren’t visit-generators — that is, people don’t typically come to your website for the first time via a landing page — a change in your conversion rates could be caused by a dip in the number of people actually visiting those pages in the first place. If that’s the case, you probably need to rethink the way you’re guiding people to those pages from the rest of your website.
On the other hand, if the number of landing page visits hasn’t changed, then you need to dig a little deeper into your data to understand what’s going on.
“We just noticed this recently at New Breed,” says Guido. “We’ve expanded our blog strategy and we’re talking about a wider range of topics, but we haven’t expanded our premium content offer [PCO] strategy along with that.”
In that case, we needed to expand our PCO library to better match the intent of our new readers.
3. Marketing & Sales Operations
Your operations, integrations and workflows need to be as smooth as possible in order to achieve substantial business growth — and yes, data analytics can help you with that, too.
Double check that:
- The right lead information is being passed from marketing to sales in your system
- The sales qualified and marketing qualified lead definitions are aligned
- The sales team is following up with leads in a timely and appropriate manner
For example, your data might tell you that your sales team isn’t following up with leads as often as you’d like them to. Sure, that could be a simple case of slack-off salespeople — but more likely, your marketing and sales teams are misaligned on what qualifies as a “good” lead.
“In that case,” suggests Guido, “you need to sit with both of those teams and help them understand each other better.”
4. Pipeline Management
Your sales pipeline comes into play in the opportunity lifecycle stage and on. Beyond the opportunity stage, there are a number of transition points you should measure, including:
- Opportunity to assessment
- Assessment to initial proposal
- Initial proposal to proposal revisions
- Revised proposal to closed-won
- Revised proposal to closed-lost
… and any other transition points that might be relevant to your business. But it’s important to note that beyond the opportunity stage, you’ve transitioned from analyzing broad marketing data to a narrower scope of one-to-one sales communication.
“That means that the way one sales rep operates can be very different from the way another sales rep operates,” explains Guido. “Understanding the differences between your sales reps is a good starting place for building your sales pipeline and improving your business.”
In other words, who’s following your sales process and methodology and who isn’t? Who are your top performers? Who is struggling to meet their sales goals?
With visibility into what works and what doesn’t, you can double-down on the good aspects of your sales methodology and prevent the bad aspects from permeating throughout the rest of your team.
“Of course, the first step to understanding all of this is measuring your opportunity-to-close rate,” says Guido. “If that metric doesn’t match up with your current goals or your historical benchmarks, you need to dig deeper.”
5. Customer Success
“One of the main pieces of inbound involves attracting good-fit leads and nurturing them into successful customers,” says Guido. “Your NPS Score should be a reflection of that.”
Your Net Promoter Score (NPS) is a customer satisfaction benchmark said to directly correlate with the health of your business. If you’re using an intentional, SEO-friendly content strategy to attract high-fit leads to your website and nurture them through the funnel with relevant, contextualized information, they’re much more likely to become a successful customer or evangelist than a low-fit, low-interest lead would.
Therefore, measuring your NPS score can give you a good understanding of the entire customer acquisition process you’ve built and whether or not it actually results in happy customers.
“The NPS is an interesting metric, though,” says Guido. “The score itself is generated by subtracting the number of detractors [unhappy customers] from the number of promoters [happy customers].”
“So, theoretically,” he continues, “you could have a scenario where your product or your services team is doing an awesome job, but your marketing team just isn’t closing the right types of customers for them. In which case, a poor NPS score is not a reflection of your product or services, but rather a reflection of your sales and marketing performance.”
Your customer lifetime value (CLV) is the other important data point to look at for customer success. Ideally, the highest NPS scores should come from the customers with the highest CLVs.
How Do You Ensure Data Quality for Accurate Analytics?
It’s no question that data analytics can help you grow your business — but that’s only if the data you’re working with is clean, comprehensive and accurate in the first place. So how do you make sure you’re collecting high-quality data?
“Neither your sales nor your marketing team is ever going to be 100 percent focused on making sure that the information they’re gathering is accurate and that the systems they have in place for collecting and tracking that information are working properly,” says Guido. “There needs to be someone who is ultimately accountable for maintaining the quality of your data.”
That someone is your business operations manager.
A dedicated, experienced business operations manager can oversee the way you collect data, the way your forms are set up, the way your leads are added to your database and any other information you collect. When you pull a report to analyze that data, everything is centralized, consistent and, most importantly, comprehensible.
“The other nice thing about having an analytics professional on your team is having an unbiased third-party opinion pulling your reports,” says Guido.
Your sales and marketing team could feel strongly about a particular report because it’s directly tied to their own goals and performance — but an outside set of eyes can help you cut through the noise and discover the real reasons behind your business performance.
In today’s competitive climate, using data analytics is table stakes for any growing company. The good news is, if you’re using any kind of marketing automation or business intelligence platform, then you’re probably already collecting this data — now, it’s just a matter of using it to your advantage. From demand generation to customer success, effective data analytics will give you what you need to accelerate your business growth.