Marketing Analytics Family Tree

Marketing analytics is a broad, “meta” field, combining elements of marketing strategy, data science, database management, digital technology, primary research, and psychology, to name a few. To help explain what it is, we’ve created this taxonomy of marketing analytics—a “family tree”—that breaks the field down from high-level to more detailed.

The taxonomy has four levels of hierarchy. The highest level splits analyses broadly into aggregated and discrete classes. Aggregated analyses look at data grouped together—for example, by month, product category, or customer segment. Discrete analyses look at the individual “data objects”—for example, leads, customers, or accounts. The next level down—call it “function”—looks at large categories of analytics that might typically be found on a Director’s business card. For example, “Director of Consumer Research”, or “Director of Customer Analytics.” The third level of the taxonomy, discipline, looks at a thematic area in that function, for example, “qualitative research” or “predictive prospecting.” Finally, at the lowest level are the specific analytics tasks or methodologies that an analyst might be doing on any given day, for example, “social listening” or “customer reactivation.”

Each task has a fairly detailed explanation of what it is below the tree. Where links to greater detail might be helpful, those have been added; but in many cases, they weren’t needed.

You’ll notice that the terms “machine learning” and “artificial intelligence” don’t appear on this taxonomy. This isn’t because they were forgotten, but these are specific techniques that can solve many of the discrete-type problems noted in the taxonomy. In some cases, specific tools are mentioned, like neuro testing, and these were included because they are so uniquely suited to a specific task.

Assuming people find this hierarchy valuable, we are absolutely open to editing it and keeping it fresh with suggested adds, deletions, or merges. Please reach out with suggestions to our Chief Analytics Officer, Andy Hasselwander, at ahasselwander@market-bridge.com.

Download a high-res, printable version of the Marketing Analytics Family Tree at the bottom of the page.

 

Marketing Analytics Family Tree_MB

Below, download a high-res, printable version of the Marketing Analytics Family Tree.

Checklist To Implementing AI-Driven Sales

At MarketBridge, we believe artificial intelligence can be used to solve an age-old challenge for sales reps – “How do I better target and how can I speed up prospect research?” But AI doesn’t have to be as complex as bots, speech recognition and machine learning for you to achieve these results. Using your existing data, predictive algorithms and workflows, you can get critical information into sales team’s hands including the best targets, the best fit products and the most relevant content to close deals.

Here is our simple checklist to delivering tangible results…

Checklist to Implementing Sales Driven AI

Sales Teams Must Adapt to the New Customer Buying Journey

Customers have clearly changed how they conduct their buying process. Their expectations have changed as it relates to their process of evaluating product or service options, self-educating, participating in decision making groups, and ultimately deciding on and making a purchase.

Customers now spend significantly more time doing online research, they expect to be served up content aligned to their buying process and on their time frames. The last thing that most of them want to do is talk to a salesperson.

As a result of this change, sales teams are struggling to engage with customers and meet their targets leading to a loss in sales productivity.

The customer is increasingly difficult to reach, leading to sales productivity metrics dropping off a cliff. When sales reps finally speak with their target, they are viewed by the customer as woefully under-prepared, primarily because today’s customer is privy to endless amounts of information and perspective prior to ever engaging with sales. Customers who are under-whelmed aren’t inviting sales reps back to take the process forward, which means the money and effort invested in reaching those customers have been wasted. As a result, sales performance is suffering, with an estimated 67% of sales reps not meeting their quotas.

This infographic outlines the trends and changes in the buyers and also provides some resources to get sales leaders started on the changing journey to realize the potential of re-envisioning the way they engage customers (and achieve improved performance).

 

New-Customer-Buying-Journey

 

9 Tips for Lead Follow Up

Leads are an integral part of any marketing and sales strategy. This issue is that it’s difficult for marketers to know which leads will convert successfully into customers. Sometimes “quality” leads can fizzle out and “suspect” leads can turn into big business.”

The best practices for lead follow up begin with a strategy. Maximizing the efficiency of the interaction and the potential of the transaction is at the core of this strategy. Your lead’s time is valuable. That said, there’s no such thing as the ideal lead, and all leads will need to be coached through the buying process to some degree. The trick is to get a quick handle on what your lead needs in order to successfully convert.

Check out the below infographic for a best practice approach to lead follow up.

9-Tips-Follow-Up-With-Leads

 

10 Ways to Improve Your Web Analytics

Marketing Accountability continues to be a hot topic, particularly in B2B circles. Although Sales & Marketing professionals have talked about the importance of data and measurement for almost twenty years, we continue to see striking oversights in basic implementation, especially as it pertains to websites.

In this infographic we highlight some basic website measurement fundamentals that will improve your web analytics implementation. With a little foresight and planning, site managers can avoid the oversights we see time and again, ensuring they can answer critical business questions about their customers.

Check out this infographic for a best practice approach to web analytics.

10-Ways-to-Improve-Analytics