Making Content Discoverable

Part 1 of Driving Success from Sales Enablement Investments Blog Series 

The original driver for the development of sales enablement platforms was the imperative to make it easier for sales people to put their hands on the right materials for a given sales situation. While the vendors have incorporated a range of additional functionality into their offerings over the last 2-3 years, solving this problem is still at the core of why most organizations decide to implement a sales enablement platform.

There are essentially 3 ways for people to discover documents, web pages, videos and other files:

  1. Browsing
  2. Searching
  3. Automated recommendation

Go-to-market Taxonomy

For any of these approaches to be effective in a sales use case, you have to give detailed thought to the way your company goes to market – your market segmentation, the personas sales people must engage with, the proposition/products you offer, the steps in your sales process, etc. Information professionals call this a ‘taxonomy’ and the right taxonomy for your particular business is the foundation for any effective sales enablement effort.

Let’s now take a look at how a taxonomy gets applied to the 3 ways documents are discovered.

Browsing

To make the way a sales person should navigate document based content self-evident, you need to implement a folder structure that reflects the go-to-market taxonomy for your organization.

The relative importance of the different dimensions in your taxonomy (e.g. industry segment, persona, product) to your business will determine how you create your folder hierarchy. For example, if industry segment is fundamental to your go-to-market, you might have this at the top level and then perhaps persona as your next level down.

As we all know only too well, trying to navigate complex, multilevel folder structures can be frustrating and often results in people giving up and asking a colleague for the last thing they used!

Searching

When you type in a few key words in the search field in Google, the Google search engine is not, of course, going out and browsing all the pages on the web there and then to find a match with the terms you have entered. What actually happens is the search engine is continually crawling the billions of pages on the web and ‘indexing’ what it finds to build up a database it can use to perform searches.

The same thing happens with sales enablement platforms. You load documents to the system and the search engine reads and analyses the textual content in each file. It also reads any categorization tags that have been added to the file (this is known as metadata).

The sophistication of the search user interface makes a huge difference to how satisfying and efficient the search process is. Being able to enter several terms with ‘and / or’ (known as Boolean) operators between them is important. This, however, requires the user to know a lot about the types of terms the search engine may have surfaced across all the content – if the search is to be efficient (doesn’t return too many hits) and effective (doesn’t miss important documents).

A more advanced approach is for the search engine to make use of an expert taxonomy loaded to it and then present the user with a number of filtering options to guide the searcher and help them refine the results. In the case of sales enablement, this is the go-to-market taxonomy mentioned above. The user interface then provides the searcher with a series of selection fields representing each dimension of the taxonomy and other attributes such as type of material (i.e. presentation, case study, brochure, white paper).

Automated Recommendation

Ideally, a sales person will not have to either browse or search for materials, they will have recommended content automatically surfaced to them.

There are two ways to do this. The first approach is to use a rules-based expert system that takes in factors for a particular sales opportunity such as industry segment, persona and step in the sales process and uses these to decide which documents to recommend for the particular sales situation.

Some systems take the input factors from the relevant fields on the opportunity record in the CRM system and display recommended files in a panel in the CRM user interface. Another approach is to use a guided selling playbook which recommends the right materials by step in the sales process and other input factors such as industry segment, persona, and product (taken from the CRM opportunity record or entered by the user in the playbook).

The second approach combines the use of an expert taxonomy to index content (as discussed under Searching above) with artificial intelligence to make the connection between the use of different materials, level of customer engagement, and successful outcomes in terms of deals sold. The system learns as content is used and deals are closed and recommends what it thinks are the materials most likely to help move the sale forward in a given sales situation.

In part 2 of this series I’m going to talk about moving from sales documents to a database of selling knowledge.

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