Four Ways to Turn Your CRM Data Into Better Business Decisions

Four Ways to Turn Your CRM Data Into Better Business Decisions

According to a BI-Survey study, 29% of respondents say that the sales department in their company plays a prominent role in data management and governance, with the finance department (60%), IT (41%) and business intelligence (41%) leading the way.

Unsurprisingly, then, sales is rarely the department that takes ownership of the numbers.

However, with SaaS technology, collecting and analyzing useful information—and, more importantly, coming to the right conclusions—can be managed by the sales team. Conclusions that could help you optimize your sales process, explains pickSaaS’s Matt Pliszka.

How big brands use data

Coca-Cola

The drinks giant uses big data to make million-dollar decisions. For example, after noticing that many consumers were adding a splash of cherry to Sprite when selecting the beverage on a Coca-Cola Freestyle, Coca-Cola released the flavor in a bottle.

It was a huge ‘a-ha’ moment for us,” said Bobby Oliver, director, Sprite and citrus brands, Coca-Cola North America, in a blog post on the company’s website. “The fact that cherry was the number-one Sprite flavor mix on Coca-Cola Freestyle inspired us to create an all-new, delicious product for fans in a convenient, on-the-go bottle.”

That’s just one small example. In fact, Coca-Cola’s use of data can get way more complicated.

The taste of the brand’s orange-flavored drinks, like Simply Orange and Minute Maid, owes its consistency to a secret algorithm. This algorithm, called the “Black Book” at Coca-Cola, takes into account crop yields, global weather patterns and more to create the perfect orange juice every time.

“[It’s] definitely one of the most complex applications of business analytics,” Revenue Analytics consultant Bob Cross, who built the algorithm, told Bloomberg. “It requires analyzing up to 1 quintillion decision variables to consistently deliver the optimal blend, despite the whims of Mother Nature.”

L’Oréal

Beauty brand L’Oréal uses brand awareness data technology to analyze tweets, Facebook posts, product reviews and news stories. When necessary, or when there’s an opportunity to build the brand, posts are routed internally to the appropriate employee who can engage with the post’s writer.

“Social media is all about real time, so we need to react in real time when it comes to customer engagement and Clarabridge helps us achieve this goal,” Céline Dumais, Vice President of Consumer Care Center at L’Oréal USA, said in a press release of Clarabridge’s website—the agency responsible for managing this social listening.

“We expect to see significant ROI through our “Voice of Beauty” mission and, most importantly, we expect to continuously improve our relationship with our customers—something you can’t put a price on.”

Westpac Banking Corp

The Australian bank has leveraged customer data interactions with the bank to improve the service they offer.

Westpac’s “Know Me” program used data from transactions, social comments and customer notes to create personalized “next best offers” for customers.

“The traditional approach is that the bank only called a customer where they had something to sell,” head of CRM and digital Karen Ganschow explained during a talk at the Gartner Business Intelligence and Information Management Summit. “Our staff and customers don’t like these calls, and it leads to a lower NPS [net promoter score] and more negative brand experience.

"Service-led communication is all about the customer; it’s about making the bank the hero to them. The reason we’re calling is all about them, and shows the bank is looking out for them as a trusted advisor. This leads to higher conversion rates.”

Coming to the right conclusion

Does data analysis always lead to the correct conclusions being made?

After all, it’s not difficult to misinterpret data, or not have enough to make an accurate conclusion.

Take Google Flu Trends, which was aimed at predicting the spread of flu based on the trend of “flu” searches online. The presumption was that if people believed they had flu symptoms, they would Google “flu” to find out.

Unfortunately, the program overestimated by more than 50% how prevalent flu would be between 2011 and 2013, according to an article in Science magazine.

So, how do you make sure you analyze the data correctly to boost your sales and grow your business?

Using sales data to grow your business

Let’s have a look at some of the CRM statistics you could use to optimize your sales process.

1. Track and optimize your deal conversions

Monitoring the success rate of each stage of the sales process is crucial. Let’s say your business has an overall “lead” to “deal won” conversion ratio of 10%. This means that 90% of your deals are lost somewhere on their way to the final stage. Tracking each lead’s flow within a CRM will help you identify and tackle the bottlenecks in your sales process.

Here’s an example: Say you’re an investment consultancy business using a CRM to handle transaction processes, which often involves scheduling specific activities for particular groups of investors. So, the stages in your pipeline could be: Contact list, Send teaser, Send presentation, Meeting and Agreement negotiations.

If 50% of your deals are lost between the “Send teaser” and “Send presentation” stages, it’s a sign that you’re doing something at this point that is putting off your customers. If the teaser and presentation are sent automatically, for example, why not try calling them as well to see what their reaction is and create another contact point.

2. Monitor activity types and use the best contact channels

If your team is accustomed to using a CRM to manage their daily routine, it can also be used as a powerful tool to optimize the specific activities and actions they take.

Each employee or salesperson can have a completely different skillset: One might be more effective using email, another might be known for closing deals in meetings or on calls. This activity data can help inform your sales recruitment and help your team develop.

Here’s an example: You run a software sales business for enterprise clients. After looking into the data, you discover that the salespeople in your team with the lowest conversion rates spend 80% of their contact time sending emails and 20% in meetings, while the best ones split their time across the two evenly.

Setting up meetings is clearly a more effective way of closing than just emailing, so you could use this data to encourage your salespeople to have more meetings with leads. In your hiring process, meanwhile, it may be worth focusing more on employing salespeople with face-to-face, interpersonal skills.

3. Work out the reasons behind lost deals

Collecting the data behind lost deals is an essential part of the process. You need to understand the reasons a deal was lost in order to make better business decisions going forward.

Mark it as a required field in your CRM and set up predefined answers to make sure you’re able to analyze the data appropriately, potentially with the help of your sales team—the fewer “Others” answers you get, the better your data analysis will be.

Here’s an example: Say you manage recruitment for a large company; you can use Pipedrive to manage HR recruitment processes. Create reasons your team can choose from when they lose a candidate, or a candidate falls through, so you can better analyze your HR strategy.

Reasons you lose a candidate in the recruitment process could be:
  • Not skilled enough
  • Not a good culture fit
  • Haven’t met financial requirements

If too many candidates are applying without the right skillset, it could encourage you to change the job description in the ad, or even try more specialized channels to advertise the opportunity.

4. Identify any inefficiencies in your team

By analyzing your team’s deal data, you’ll be able to pinpoint where in the sales process they may not be performing as well as they could be.

Maybe you’ve assigned too many accounts to your salespeople and they’re not able to manage all of them, or maybe you have one rep who is finding loads of good leads but can’t convert them.

If you are managing a large enough sales team, this could be a good reason to consider a different structure. With the assembly line structure, you could place this rep in the team responsible for acquiring new leads, and make other reps with higher close ratios responsible for converting leads into customers

Here’s an example: You run an IT services business and you notice that 80% of the calls planned by your salespeople are not being completed. Perhaps you should check if your salespeople have the necessary resources and software to make calls/video calls.

Alternatively, they could be too busy performing other formal tasks, which might mean it’s time to hire an additional salesperson to make sure more planned calls are executed.

Are you making the most of the information you collect?

Managing a business without the data is like driving a car on a journey without a destination. It might be fun to do but you’re unlikely to end up where you want and you may lose opportunities along the way.

Keeping track of all the activities in your CRM is the first step to improving your business decision processes based on data. The earlier you start measuring the data using SaaS software, the more information you’ll have to back up your decisions.