Case Study: The Potential of Your Data Paying Off in a Big Way

Case Study: The Potential of Your Data Paying Off in a Big Way

This is a short case study of a CT business with a challenge faced by many companies: Using their own data, how could they develop a more predictable sales funnel by monetizing data, one which results in revenue gains that are achieved more reliably and cost-effectively? And how could they optimize for actual sales rather than just leads, most of which turn out to be of poor quality?

The company’s primary business is selling industrial products through its website to other businesses, many of whom become repeat purchasers. Here is how they approached the challenge and what the results were.

Getting Started: They began with two steps – 1) determining what useful data they currently accumulate, and 2) deciding what key questions they would love to be able to better answer.

Available data they own that ties directly to actions/behaviors of prospects and customers, and the software that generates each:

  • logs & reports detailing visitor activity on their website (Google Analytics, or GA)
  • CRM data (Salesforce)
  • customer service/help desk data (Freshdesk)
  • email marketing data (MailChimp)
  • customer survey data (SurveyMonkey)

It was determined that the data is relatively clean, well-organized and certainly useful for helping to better answer these questions:

  • What is the profile of our best customer?
  • What is the profile of our worst customer, the ones we wish would go away?
  • How do we get from “unqualified lead” to “paying customer” faster?
  • How do we build stronger customer loyalty and retain quality customers longer?
  • How do we drive more revenue while incurring less sales & marketing expense?

Connecting Silos: Once the data sources and unanswered questions were identified, the next step was determining how much of the data can easily be linked in an automated way, i.e. Google Analytics data exported to and imported from Salesforce. Fortunately for this company, all of it can be integrated. They chose to initially focus on the data generated by three of the apps — GA, Salesforce and MailChimp.

A web analytics tool like GA can be very useful for seeing how a visitor interacts with your site and eventually becomes a customer. A CRM system can be equally as useful at tracking activity as a lead closes and a new customer is managed throughout its lifecycle. And marketing automation tools such as MailChimp can substantially boost the effectiveness of customer outreach efforts. All three generate valuable data and have good reporting capabilities. The problem is that most companies view and analyze these three sets in a vacuum, not in a combined way. This makes it very difficult to gain a full picture of what is and is not working with inbound marketing, sales and outbound marketing. The piece that’s missing is linking and correlating the three data sources such that when the data is combined a much more insightful understanding of customer or prospect behavior emerges.

The company did exactly this, and here’s one example of what it meant in terms of tangible improvement:

MailChimp is used to broadcast emails to a small number of customer and prospect lists. When a recipient clicks on a link to their website within the email, that person is then tracked (with a uniquely assigned ID number) each time he visits the site. The pages he visits, the length of time spent on the site, where on the internet he was before accessing their site and other details are all captured by Google Analytics. Some of this data is then automatically fed into Salesforce. Within Salesforce, every visitor that clicked a link to their site is given a score of 1 to 5 based on how much they interact with the site (5 = heavy interaction, down to 1 = no interaction, exited after one page). Individual scores do change over time and are combined with other factors to allow highly segmented email lists to be developed.

The segmentation is critical for content to be created that is relevant to a specific group, i.e. prospects that have visited a particular product page multiple times but have yet to buy. This type of insight and ability to target individual visitors would not be possible without the sharing of data between GA, Salesforce and MailChimp. Just being able to know, for example, that a cold lead (whose email address they have) returned to their site a second time at a later date is incredibly valuable to this company. It allows them to take fast, specific action to try and turn a cold lead into a customer far more quickly than they could in the past.

Answering The Tough Questions: With 3 key data sources integrated, the company was in a good position to better understand what behaviors and characteristics are common to high-quality leads and ultimately to best customers. To accomplish this, a higher level of analytics was needed that enables correlations to be drawn between data that can seemingly be unrelated but in reality uncovers distinctive patterns and trends in buying behavior. The findings revealed a lot of new information:

Common traits of highest quality leads:

  • Access ‘About Us’ page
  • Remain on the site > 90 seconds and access 5+ pages, on average
  • Download PDFs containing product specs
  • Utilize ‘Live Chat’ option to ask questions
  • Visit blog page

Common traits of best customers:

  • Female
  • Open company’s email marketing messages more than 50% of the time
  • Buy both list-price and discounted products
  • Respond to survey requests
  • Prefer Live Chat and other self-service options over phone contact
  • Generate unsolicited endorsements (online)
  • Click links contained in emails to view multiple-product bundles offered at reduced rates
  • Pay on time (if off-line account has been established)

Overall Results and Conclusions: Although in place for just the past 12 months, the company believes they have already benefitted significantly from this new data program. Some key things they have achieved and concluded:

  • Eliminated the practice (and cost) of paying for email lists. Quality email lists that haven’t been endlessly spammed are no longer for sale. Paid-for lists yield too many poor-quality, waste-of-time “leads”.
  • Expanded from 4 to 12 email marketing lists (and growing). Each is segmented towards a specific customer type, prospect profile, product family, etc. and content in now much more refined for individual lists. On average, email open rates have improved from 11% to 24% for prospects and 18% to 37% for customers.
  • Sped up the average time to determine whether a lead is qualified or unqualified. In turn, this has accelerated their process of engaging a qualified lead with “personalized” information and decreased the average time to convert a lead to a paying customer from 88 to 56 days.
  • Increased the total number of return buyers and their average spend, while the churn rate (defined as longer than 180 days without purchasing) dropped from 18% to 10%. They attribute this directly to customer satisfaction and loyalty gains that come from much more relevant content being shared at the individual customer level.

Next Steps: Already underway is integrating more customer service data (Freshdesk) and customer survey data (SurveyMonkey) into the mix. This will allow for even more personalized content to be created based on direct customer input. The ultimate goal of this company is for customers to sense that a true one-to-one relationship exists between the two businesses, one in which the signals a customer provides are responded to with information and offers tailored to their individual needs.

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