3 simple data analyses projects to inform your business growth strategies
This month, our case studies series continues with a case study on our portfolio company VersionOne. We will explain how their management teams leveraged our sales and marketing support to optimize their marketing and sales strategies, which helped sustain high revenue growth over the last 3 years.
I just want to highlight 3 projects I think are extremely valuable among those described in the case study. They are relatively easy to execute and do not require a lot of resources. My point, which hopefully is proved here, is that while we offer these as part of our strategic consulting services, they are completely straightforward, which allows any startup the ability to implement the outline described here and execute it successfully on their own.
The first is a lead source and lead source combination analysis:
Input: – Data export from web analytics and CRM system – it is crucial that the CRM system houses reliable records of the referring site/keywords where the lead originated.
– Ranking of lead sources by volume, conversion rate and revenue generated.
– Ranking of lead source combination (if the same prospect revisits the website multiple times and is converted multiple times via different sources) by volume, conversion rate and revenue generated.
– Correlating web analytics data with the CRM system to identify most important lead sources and assign them to each lead
– Use cross tab to analyze combinations of lead sources (lead paths) in terms of volume, conversion rate and revenue generated
– Better lead sources in which to invest
– Best combination of lead sources to be used in conjunction together
The second is a lead scoring analysis, which is covered in detail in the case study
Input: – Data export from CRM with all lead fields and conversion information (opportunity created, opportunity lost/won)
Output: – Formula for calculating a ranking, so that leads most likely to convert are ranked on top
Method: – Perform cross tab analysis of lead conversion data versus lead information fields to identify factors which affect potential conversion.
– Build up the ranking formula by adding the identified factors together, one by one, and comparing the predicted conversion with the actual data to gauge the ranking’s efficacy.
– Refine and validate the ranking formula with a fresh set of data.
Insight: – What are the top 20% of leads that eventually convert to 80% of the opportunity?
– What are the lowest 20% of leads that will only contribute to 1% of the opportunity value created?
The third is a lead conversion path analysis, which is an analysis of the typical path of a web visitor on the website from the landing page until the exit page or the conversion page. This is more involved than the simple one page conversion analysis – it accounts for different visitor’s behaviors and analyzes the website more holistically.
Input: – Web Analytics Data
Output: – Key conversion points: web forms, call to action, important product pages
– Typical conversion paths, with volumes and conversion rates at each key conversion point
Method: – Segmenting traffic by the pages visited, analyzing the flow of traffic in and out of each page, calculating conversion rates at critical pages/forms, calculating action rates at critical call to action items.
Insight: – What are the most effective conversion points?
– What are the weakest conversion points and why?
– How should we optimize the website visitor flow on the site to ensure optimal conversion?