Guest Post : The CRM and the pre-contact data challenge
I recently faced a data challenge that almost disturbed a whole organization. My aims were honorable, I insist, but this was something so new, so unprecedented, that it nearly didn’t happen. When it did, it revolutionized the marketing to sales hand-off and a number of processes in between. It gave us an extra layer of rich detail that enhanced our marketing and sales processes, with market information that gave us real focus.
Stage 1 – get the data
There are a host of IP tracking software programs available, some better than others. We selected one which provided a short piece of code to put on our website, which tracked the IP address of the businesses surfing our website, and matched them to Dunn & Bradstreet databases, as well as LinkedIn. Immediately, we had a flow of data; from the 1500 visitors who hit the site each day, we could identify at least 300 of them. The system would give us key contact data from LinkedIn – in our case; we wanted the Human Resources and the Finance Directors, as well as managers.
However, it would also give us an extra rich seam of data to work with – what pages they were looking at, which search terms they had used, how long they had been on the site, and how many times they had visited in total. These metrics allowed us to gauge their level of engagement with the website but also what they were interested in. When a leading airline came on looking specifically for what we provided, we knew there was something powerful here.
Stage 2 – filter the data
What we quickly realized was that the data we were getting through was of huge value, but only if we were able to quickly filter through it for opportunities. We extracted our own database into .csv format and filtered that into customers, prospects (i.e. leads), and suspects (i.e. no contact has been made). Within customers, we broke that up into international, safe, and at risk so that we could better understand why our customers were on the website.
That data was then uploaded into the system with a match against the business name, and each organization was then tagged appropriately. Naturally, data sets include variants, so a secondary match was made for those with no match against the business address in order to ensure that as many of the organizations as possible were tagged. We used a color-coding system to quickly identify our web visitors.
Stage 3 – identify what matters
We set up a steering committee to understand exactly what we wanted to get out of this data and how we were going to use it. For the first time, we were able to identify when a potential lead made first contact – but we needed to identify exactly what we required and how we would use it.
Clearly, search information was key – this gave us an idea of the user’s intent. In one instance, we identified a leading hospitality chain before they went out to tender, knowing exactly what they were looking for before they told us. That allowed us to approach them. Therefore, matching a business name to a keyword was crucial.
Engagement metrics were equally key – we didn’t want to know a business that had left after just one page or one that had not returned. Therefore, we needed to filter the data into the “most engaged” businesses, using the number of visits, and the number of pages and equally using triggers to see businesses who had visited certain pages.
Stage 4 – set up the CRM to drive the project
We were using Microsoft Dynamics CRM, which allowed us to create bespoke events that would be visible in the 360-degree dashboard. Of course, this would have to be at the organization level, as IP addresses cannot be matched to contacts directly. What we needed was a new “web visit” event matched against organizations, with key information such as keywords, titles of pages visited, depth of visit, and the total number of visits.
Rather than setting triggers up in the tracking software itself, we used the CRM to automatically trigger an alert to the appropriate business development manager whenever a web visit was triggered in the system. So, for example, if a business of 500 employees in the South-East recorded a 6-page visit on a key search phrase, then the South-East mid-corporate business development manager was alerted.
Stage 5 – getting the data back into the CRM
The key here was automating the process – and it required a little bit of trickery in the background to ensure that it worked seamlessly. On a daily basis, the system would do a batch data upload into the CRM using a cross-match of data fields that transferred vital information from the tracking system online, into the CRM.
Equally, where contacts were missing from an organization, we would receive an alert that would allow us to manually update the contacts ourselves at a later date.
Stage 6 – getting buy-in & refining the process
There were bound to be initial problems – for example, some sales representatives resented receiving too much information about potential prospects they had no interest in. However, upon receiving the aforementioned tip-off about the hospitality chain, a salesperson decided to act and was able to use the search phrase information to build a conversation with the business.
Sales agreed that it was best to collect data over the long term and get a better appreciation of the prospect’s buying process. For example, contacting the prospect too soon in the process could potentially be negative – the CRM was throwing up extra intelligence that showed prospects were likely to visit the website at least five times before making an inquiry. What we needed was a trigger to say, “this business should have contacted us by now,” – and that was when we had to act.
Stage 7 – building intelligence into marketing efforts
Events and triggers from the Dynamics CRM allowed us to better inform sales of when a potentially sales-ready prospect had been on our website, but there was a further layer of information that we could use here – the pre-contact buying process. We had learned that contacts were using key phrases of a generic nature in order to find out about the service in general, and then they were diving into deeper information, with white papers and PDF downloads being crucial at this stage. At a later date, they would return with a brand phrase – either brand alone or brand + service.
We could then take this information to make soft contact with the organization, either by direct mail or telemarketing, in order to gauge their true level of interest. We would never mention the fact that we knew what they had been looking at (it sounds a little big brother!), but our conversion rates on direct and telemarketing rose substantially as a result of this intelligence.
The CRM allowed us to extract data according to the most recent visit and the number of visits to the site, and we could then prioritize our efforts according to the depth of visit and keyword intent, personalizing the message further. The additional data provided by the system, cross-fed into the CRM, allowed us to further enhance our contact possibilities.
And at the heart of this…
We believe that CRM is the cultural glue of an organization. You only get out what you put in, and that was the mantra that every department in our business repeated – from the quality of the data to the depth and richness of that data. By adding in a layer of pre-contact intelligence and looking at the models of engagement with our website, we were able to build an observe-and-contact model that both sales and marketing could use in order to better understand and approach a potential prospect with a view to getting a face-to-face appointment.
As we go on, we are beginning to weave new aspects into our contact strategy, from building social media feeds to get a greater appreciation of customer needs. For example, there is potential for upsell when a customer visits the website, looking at extra product or service pages. We did not have that capability before because it was not spoken about.
At the very heart of this was the CRM. The glue that binds the organization, the tool with which to interpret and communicate this intelligence, and drive what ultimately became a stellar year for sales.
Gareth Cartman writes frequently on business, HR and CRM topics, and works with MS Dynamics partner Preact, who are based in the UK and were recently awarded President’s Club status by Microsoft.
Tags: Blog, Challenge, CRM, Guest Post, Pre-contact data