In a conversation with Alex Bard, CEO of Assistly (now desk.com, part of salesforce.com), I learned a few interesting things about customer service solutions for small to medium-size businesses (SMBs): (1) Companies can be too small to have customer service organizations; (2) the main competition of vendors of SMB customer service solutions is not each other, but Post-It notes and Gmail; and (3) the service that SMB customers demand is exactly like the service that enterprise customers demand.
So what do each of these points mean?
Companies can be too small to have customer service organizations. Without a formal customer service organization, customer-facing personnel such as customer relations managers, CEOs, and marketing folks are on the hook to answer customer inquiries. These employees wear many hats, are on the road a lot, and communicate constantly with one another. And, more than likely, their companies also don’t have formal IT organizations. This means that customer service software must be tailored to a business user: easy to deploy, easy to configure, and supporting a multitude of mobile devices. Customer service software must also have built-in collaboration features, alerts, and notifications allowing personnel to quickly work together on a customer issue for quick resolution.
Customer service managers don’t often realize that data quality projects move the needle on customer satisfaction. In a recent Forrester survey of members of the Association of Business Process Management Professionals (ABPMP), of the 45% who reported that they are working on improving CRM processes, only 38% have evaluated the impact that poor-quality data has on the effectiveness of these processes. And of the 37% of respondents working on customer experience for external-facing processes, only 30% proactively monitor data quality impacts. That’s no good; lack of attention to data quality leads to a set of problems:
Garbage in/garbage out erodes customer satisfaction. Agents need the right data about their customers, purchases, and prior service history at the right point in the service cycle to deliver the right answers. But when their tool sets pull data from low-quality data sources, agents don’t have the right information to answer their customers. An international bank, for example, could not meet its customer satisfaction goals because agents in its 23 contact centers all followed different operational processes, using up to 18 different apps — many of which contained duplicate data — to serve a single customer.
Lack of trust in data negatively affects agent productivity. Agents start to question the validity of the underlying data when data inconsistencies are left unchecked. This means that agents often ask a customer to validate product, service, and customer data during an interaction — increasing handle times and eroding trust.