Sales teams rely on a variety of datasets these days. Prospects expect a high degree of customization, and data enables sales teams to deliver this experience. Sales analytics can play a major role in delivering customization, but using this data well can also be a challenge.
Companies spend a ton of resources gathering data, but a lack of data science training can render these efforts obsolete. For instance, sales teams might configure their CRMs to collect a wide range of metrics and behavioral signals, but what if they analyze the wrong figures?
In this article, we’ll look at four key data sources and the relevant metrics within them that sales analytics teams must look at. If you’re interested in diving deeper into data science and analysis, check out our course on Wrestling with Data.
Every business conducts the majority of its marketing and sales operations online these days. The good news is that this online push has made sales and marketing a scientific endeavor. Sales teams don’t have to guess what their prospects want anymore, since online data gives them this information. However, the challenge is sifting through these datasets and zeroing in on relevant information.
For starters, trend analysis is essential when compiling prospect outreach strategies. Search trends and paid ad spending trends highlight key issues in the industry. For instance, a prospect might have increased their spending on a particular keyword while their competitors moved away from it. This points to a gap in the prospect’s strategy that the sales team can leverage.
Demographic changes can also be measured thanks to traffic from social media platforms. Every social media platform attracts users who exhibit certain qualities. For instance, TikTok attracts a young audience, while Pinterest attracts a primarily female, high-earning crowd. A sales team that is looking to pitch its product as a solution must analyze the prospect’s customer demographic and get to know them better.
Demographic and trends data can also help sales teams figure out whether their marketing strategies are working. For instance, is marketing addressing the right pain points with collateral? Digital footprint datasets reveal the answers to these questions.
Engagement is a pillar on which marketing and sales strategies are built. These datasets let sales teams know how well they’re connecting with their audience or if there’s an expectation gap. Engagement metrics change as we move deeper into the funnel. At the top of the funnel, traffic and unique visits point to high engagement and successful campaigns.
However, these metrics aren’t as relevant deeper in the funnel since the objective is to convert prospects to customers. Metrics such as form signups, demo requests, and sales queries are far more important. Examining the context behind engagement is also important.
For instance, a whitepaper might be downloaded tons of times, but how well do those prospects re-engage? A majority of those downloads might have come from people just looking for information, and not product-specific prospects. As such, a high conversion rate as measured by downloads is irrelevant.
In this scenario, teams need to evaluate whether the whitepaper’s subject is funnel-appropriate or not. Traffic sources must also be classified in context when looking at conversions. For example, source A might provide more downloads, but source B prospects might provide more bottom-line revenue, the ultimate objective. In this case, devoting resources to source B makes more sense.
Every great sales team is backed by an equally great CRM database. A company might have a great product and a great sales team, but if they don’t know who to reach out to and pitch, their efforts are for naught. Understanding who to reach out to is a function of understanding the prospect’s buyer journey.
For instance, one prospect might have a highly bureaucratic process that involves multiple signatories while another might involve just two. In such cases, tailoring outreach pitches and figuring out what content to share is critical.
Thanks to different workflows, sales cycle duration metrics will also differ.
A good CRM database will help sales teams understand their customer better. Job titles and reporting hierarchies will reveal the decision flow at the prospect, along with company size and other relevant data. While CRM data won’t illuminate buyer journeys by themselves, they will remove the prospect of reaching out to the wrong person.
Competitor datasets help sales teams bring context to their outreach efforts. For instance, they help teams benchmark their prospects’ efforts and uncover gaps in their strategies. Competitor data also highlights the latest industry trends. These trends will affect product development and the pitch a salesperson will adopt when reaching out.
Certain competitor moves can add value to the way sales teams approach a prospect. For instance, a competitor moving to a new advertising channel might not directly impact a prospect’s product use.
However, highlighting this information helps a salesperson exhibit industry knowledge that builds closer relationships.
Collecting data is relatively simple these days but analyzing it to drive insights is tough. By identifying the right datasets to dig into, sales teams can reduce the time to analyze data and increase their performance. The result is more sales, reduced sales cycle times, and better customer relationships.