Decoding marketing and sales performance can seem at times like an arcane art. There are plenty of business intelligence platforms that can return buyer behavior metrics. However, translating this raw data into insightful information that drives correct decision-making is much harder. In this piece, we’ll look at some of the ways the buyer journey can be measured and interpreted throughout the buying process.
What Data Can We Measure From Our Funnel?
A typical buyer journey funnel starts with the brand or product awareness and narrows as the buyer gradually draws toward a purchase. Let’s consider the data sources sales and marketing professionals can draw upon for each stage:
The buyer first encounters the product, usually online. Metrics that help identify leads may include landing page visits, time spent on the page, and click-throughs.
At this stage, the buyer is forming their opinion of the product. This can be observed and measured by looking at the time spent on product pages, queries made via contact forms, and pricing page behavior (how long do they spend there?).
The buyer here is close to making a decision. They may seek further information. Indicators of consideration might include interactions with chatbots, whether they have come from a competitor site, and the time spent on product pages. The buyer may have their first interactions with sales reps. They may opt for demo downloads, and a key metric here is how much time they spend trying out any demos.
Here, the buyer expresses a strong preference. Sentiment analysis can be applied to recorded conversations with reps. Recurring pain points can be identified. Marketing and sales teams benefit from demo feedback, and you can also look at buyers’ preferred pricing tiers and their ongoing interaction with sales reps. Some insights can be derived from behavioral data, but it should become apparent to sales teams if prospects are likely to convert.
Once a purchase has been made, useful data can still be gleaned from the pricing tier selected, plus adoption and usage stats, churn or renewal of subscriptions, upgrades, and other ongoing buyer behavior.
Some of the above information is easy enough for sales and marketing personnel to obtain from their own interactions with leads and prospects. Other pieces of information must be extracted from landing page behavior or usage data (for example, when a demo is downloaded). The danger lies in all the lost interactions: the early-stage site visits and browsing behavior that indicates initial interest. If sales teams don’t pick up on this behavior, they miss the opportunity to significantly widen the mouth of the buyer funnel.
Fortunately, available data analytic tools can help draw in a large volume of behavioral data from these early stages. What happens next is crucial. This information must be turned into meaningful metrics.
RELATED: What is Buyer Journey Mapping?
Key Metrics of the Buyer Journey
Much of the metrics that growth managers care about concern sales rep performance. Forbes magazine describes the predictive KPIs that help sales teams improve, calling these metrics leading indicators. These tend to include:
- Number of prospects in the pipeline
- Time spent prospecting
- Total value of all current prospects
- Conversion rate from leads to buyers
- Number of demos or presentations given
- Churn rate (subscriptions dropped or not renewed)
Although these can be helpful indicators of team performance, they miss out on the non-human aspects of the buyers journey — such as landing page content and design, pricing strategy, demand strategy, advertising, competitor performance, and product design — that can make all the difference to the top of the funnel.
Business intelligence platforms help by digging into these early-stage behaviors for additional insight that can help shape the way reps approach prospects to drive towards conversion. Here are some typical data-led early funnel metrics:
Point of Visitor Origin:
Referral traffic stats (such as those obtainable from Google Analytics) can help you understand your potential competitors since many site visitors will be visiting a series of rivals before making an informed choice. You can also tell how important review aggregator sites are and how effective your SEO strategies are.
Average Time on Page:
This can tell you how engaging your content is, including product and service descriptions and blog content.
Average Session Length:
This is another measure that Google Analytics and other tools can provide, indicating engagement based on the total time spent on a particular site.
This metric measures the effectiveness of the links and buttons in emails, on social media, and onsite, which lead to deeper engagement on the part of the buyer. This will tell you how impactful your marketing strategies are.
This is an accurate indicator of strong interest and should act as a pool of potential conversions. If this metric can be broken down by demographics, it can be very revealing.
Average Call Durations:
Are you talking to prospects? Has this time increased or decreased? Do you require a greater number of calls to secure a conversion? Patterns in this metric can indicate changes in the market and sales rep performance.
Is your drip campaign performing well? Are people reading your emails? How many of them make it into in-boxes in the first place? This kind of information can help redesign effective campaigns.
What proportion of demos, free trials, and other deal sweeteners are taken up? If demos or freemium subscriptions are accepted, how many of those users convert to paid tiers?
This is not an exhaustive list but is intended to highlight the information that data can give you about buyer behavior. There’s another pool of data you can draw upon, of course – historical sales data. We’ll look at the importance of that next.
Applying AI-Driven Analytics to Historic Marketing and Sales Data
Many popular business information and decision intelligence platforms use AI and machine learning to derive insights from large pools of historical sales data. By looking carefully at what sales strategies resulted in conversions and examining millions of data points, these AI-driven systems derive actionable insights that work.
The Problem of Choice
One of the best things about the 21st century sales and marketing environment is its multichannel approach. When you have calls, emails, videoconferencing, SMS messaging, social media contacts, chatbots, and other resources at your fingertips to progress a sale, where do you start?
With reps juggling dozens or even hundreds of prospects at once, all at different stages of the pipeline and using an assortment of communication channels, it can feel like spinning plates. Without a method for designing and choosing effective playbooks, there can be a sense of paralysis.
Fortunately, data analytic tools thrive on choice, large data pools, and a range of possible strategies to draw upon. They analyze various courses of action that have proven successful in potential scenarios. They offer solutions that are likely to achieve success, backed up by statistical data. On the few occasions when an AI-derived strategy doesn’t work (there are always outliers), it won’t be the rep to blame.
These decision intelligence systems can also prioritize contacts, again based on a thorough analysis of the historical data. They can also help with the ranking of prospects, informing reps how many days to leave between an email and a phone call.
The best solutions bring marketing and sales together, in partnership so that marketing can carry a heavier load on certain prospects and activities to move deals down funnel faster, while sales focuses heavily on the opportunities highest likelihood to close today.
Common Reasons for Buyer Hesitancy
Decision intelligence reveals the most common reasons why buyers get stuck in the funnel or leave it entirely. Here are just some of them:
- Objections were not addressed. A recent article in Harvard Business Review noted, “Where average performers look to avoid discussing concerns and objections for fear that bad news will ruin their chances for a close, high performers look to actively engage customers to surface unarticulated objections so that they can address these concerns head-on.”
- The price wasn’t right. Reps need to be working within realistic discount parameters, and data analytics can be beneficial in finding the right pricing sweet spot.
- The product didn’t solve a problem. Essentially, your prospect didn’t feel they needed the product. It was, at best, a “nice to have” rather than an essential purchase. This is where statistics, testimonials, white papers, and other persuasive content can be helpful.
- The rep wasn’t trustworthy. This can come down to training, attitude, charisma, and sometimes there’s simply an unavoidable mismatch between buyer and seller. Performance metrics can tell managers whether there’s a real problem or the sales pitch itself that’s not working.
Many of these friction points can be reduced with better data and the predictive power of decision intelligence.
Decision Intelligence: Full Visibility across your Entire Buyer Funnel
When companies invest in AI-driven decision intelligence, they can start building a culture where effort is focused on all parts of the sales funnel, maximizing impact. Such companies effectively get a “magic marketing button,” which tells them which strategies to adopt at critical moments in the buyer’s journey.
In 2020, Forbes found that 54% of businesses stated that business intelligence was crucial for deriving future strategies. As a practical application of business intelligence, decision intelligence contributes to the success of thousands of leading brands.
RevOptics provides a decision intelligence solution that has both predictive and prescriptive power. It tells employees where to focus their attention, plus which communication channels and techniques to employ. It visually illustrates the success of your marketing and sales efforts and highlights areas for improvement. Request a demo today to see what RevOptics can do for you.