CRM systems are designed to automate processes, improve customer responsiveness and increase visibility. Your teams can’t wait to get their hands on the new toolset when they think about having more accurate, secure data at their fingertips. Post-implementation, fast forward a few months and getting your salespeople to properly use your new, shiny CRM is like pulling teeth. So, what causes the disconnect?
Industry analysts generally estimate CRM project failure rates to be between 30% and 70% and user adoption – particularly seller user adoption – is the primary cause of project failure. There are thousands of articles and an entire industry dedicated to addressing this issue, and the common theme amongst them is this: the persistently high failure rates of CRM implementations demonstrate that the challenge is not easily overcome.
Why CRM Fails Sales
Process automation is foundational to CRM, which is inherently beneficial to marketing and customer service teams. Since CRM makes their jobs easier and improves the quality of their work, customer service and marketing teams are quick to embrace CRM. But for salespeople, whose focus is working with customers and driving revenue, CRM is generally perceived as a major disruption, leading to a lack of user adoption and ultimately, project failure.
Lack of Value
Theoretically, CRM systems are extremely beneficial to salespeople. Process automation provides organized and accessible customer and opportunity data, which significantly increases sales effectiveness — and formal sales process automation is proven to increase revenue. Businesses expect their CRM implementation to improve close and win ratios, in turn leading to higher commissions…another benefit for the sales team. But, unless there is obvious and rapid value for sellers in using the system and it can demonstrably improve their work experience, no amount of training, positive, or negative reinforcement is going to drive anything more than the minimal level of adoption.
Time Consuming (and boring) Data Entry
Typically, entering data in CRM is boring, time consuming, and contrary to the action-oriented nature of salespeople, which is why CRM fails to meet the expectations of businesses far too often.
Salespeople are generally reluctant and, at best, “just-in-time” data entry clerks. When demands for data exceed what sellers feel is necessary at the time, they start skipping steps and may or may not catch up to them later in the sales cycle. Many will “batch” their CRM entries daily or weekly, relying on memory or sketchy notes.
Salespeople Don’t Have All the Data
Business’ email and calendar systems are swimming with useful information about customers, opportunities and prospects. What topics are they discussing? What information are they sharing? What content are they engaging with? The holders of this information, many times, are not users of the system. Therefore, in a traditional CRM system, the salesperson would have to collect this data from other team members, manually organize it, code or assign it, and enter it into the system.
This is not a likely scenario for any individual seller and certainly not a scalable solution for any business.
How to Increase CRM User Adoption Rates
To address this issue, many businesses customize CRM systems with sales activities so that data input is part of the natural flow of the sales process. With enough investment, this strategy can work for your inside sales team. However, when it comes to outside sales or big-ticket sales, this is not a viable option.
Integrating AI and CRM
Enhancing your CRM system with artificial intelligence eliminates time consuming tasks and enables sales teams to spend more time in front of customers working on revenue generating tasks. When integrating AI and CRM, you should look for a solution, such as SalesConnect 365 that provides:
Automated Data Capture: The solution should crawl your systems such as Microsoft Office 365 or Exchange, Dynamics 365 for Sales, SharePoint, Teams, LinkedIn, etc., retrieving all data related to leads, customers, prospects, and opportunities including emails, meeting details, and documents.
Record Enrichment: New contacts should be associated with accounts and opportunities. Leads, contacts, accounts, and opportunities should be automatically enriched with all relevant data and activities.
Analysis: Integrating AI with CRM should provide continuous analysis with vast collections of customer interactions which are organized, assessed, and scored based on dynamic, objective criteria.