Turning Contact Center Analytics Into ActionsTurning Contact Center Analytics Into Actions
Cloud-based contact center solutions can fuel personalized experiences by surfacing CRM information about customers as they begin interacting with agents.
August 1, 2017
For more than 20 years, technology companies have espoused the benefits of customer relationship management (CRM) software to improve customer relationships. Companies like Salesforce have been instrumental in evangelizing the importance of improving customer relationships while reducing the cost of doing so through cloud-based technology. But CRM solutions alone are not enough to drive increased engagement.
Accordingly, cloud-based contact center solutions have emerged as key strategic components of the technology portfolio of customer-centric organizations. Coupled with the strategic deployment of CRM, contact center solutions provide a critical bridge between customers and an organization's agents, capable of driving levels of customer engagement and intimacy to new heights.
Cloud contact center solutions can fuel personalized experiences by instantly surfacing information, stored in CRM solutions like Salesforce, about customers as they begin interacting with agents. These modern contact center solutions also can dynamically route a customer interaction (voice call, chat session, email inquiry, etc.) to the agent best equipped to address a given issue. Critical to this feature is the ability to route all traffic -- regardless of channel -- according to the same routing rules and as implemented in a single "brain" or universal contact distributor (UCD).
To accomplish this level of personalization, modern contact center solutions must be deeply and seamlessly integrated into an organization's CRM solution along multiple dimensions.
The CRM desktop should be the only agent interface, to enable efficiency while interacting with customers. To avoid confusion, agent presence-awareness must persist between the CRM and contact center solutions. Alternative approaches featuring distinct CRM and contact center desktops and agent presence can drag efficiency down significantly, leading to customer frustration.
The CRM database should be the only repository for customer data, including that related to customers' contact with the organization -- the when, the why, and the what of contact center interactions. Storing data in both the CRM and contact center solutions creates islands of information that make data mining and reporting difficult and costly.
Modern contact center solutions should be capable of handling customer engagement across the customer lifecycle, from marketing to selling to servicing. Too often contact center solutions are built primarily for inbound requests and don't effectively handle outbound (inside sales) scenarios. Some may offer outbound capabilities but cannot feed prospect identity data to marketing tools like Marketo or Salesforce Pardot to tie together identities and campaigns. Going forward, contact center solutions will need to address multiple simultaneous use cases.
Interaction analytics should execute within the CRM solution. Interaction analytics is the analysis of customer interactions (e.g., contacts into the contact center) to gauge levels of customer engagement. Interaction analytics involves not only analysis of the metadata about interactions (when, how many, what channels, problems solved, products sold through the contact center, etc.) but also analysis of what happened across interactions. Ten years ago, centralized on-premises data warehouses were popularly believed to be the best way to store and analyze data, but they almost never achieved economic payback. Today, the advancement of cloud technologies has largely obviated the need for on-premises warehouses -- and there are multiple reasons why analytics should occur in the CRM solution. One is the fact that centralizing all enterprise data, which typically involves the movement, transformation, and normalization of data, reduces the veracity or trustworthiness of data as it is manipulated, lowering overall data quality over time. Further, customer data already resides in the CRM solution, and because most analyses will involve both customer data and information on the way they interact, having the analytics execute within the CRM databased makes sense. Lastly, today's CRM systems come equipped with robust dashboard and reporting capabilities with which IT staff are already familiar.
Continue to next page for a discussion on speech analytics
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Speech Analytics: Voice Sensor of the Customer
For interactions that take place on the voice channel, contact center solutions must come equipped with embedded speech analytics able to mine data from what customers and agents are saying.
Speech analytics describes the ability to analyze the voice (through automatic speech-to-text transcription) and other types of customer interactions (chat sessions, email correspondence, etc.) through the contact center. Speech analytics includes the detection of attributes such as customer sentiment, call drivers, competitive insights, and agent effectiveness that assist in understanding what customers are saying and how agents are interacting with them.
Speech analytics removes the need for supervisors or quality management personnel to manually inspect individual customer calls or interactions as they would previously have done. It provides an engine capable of surfacing macro-level insights about what all customers and agents are communicating across all interactions. Speech analytics engines can push data into the underlying CRM solution -- and its embedded artificial intelligence (AI) engine -- for discovery of deeper patterns and levels of insight.
AI is becoming a critical component in generating and discovering customer engagement insights. Most organizations have plenty of data but not enough access to the right people who know which questions to ask of the data. The good news is emerging technologies like Salesforce Einstein and IBM Watson can ease the ability to apply AI in specific uses to surface insights without the need to know which questions to ask.
With Salesforce Einstein, for example, a company could analyze customer data through the contact center, including the best people to pitch, what products agents should pitch them, the best time to pitch them, and so on.
IBM Watson can provide an efficient engine for surfacing intelligence from non-customer data, for uses such as providing expert advice on products or support through the contact center. Using deep insight, Watson can use chat bots to deflect calls from the contact center, thereby driving down human capital costs. Watson can analyze which questions have been asked in the past, which answers solved the problem the fastest, and more.
Contact center solutions feed interaction analytics data into CRM and AI engines, which can efficiently access and process data to measure customer engagement. In computer science terms, this is moving the data to the compute, not the compute to the data. Be wary of contact center or inside sales platforms that espouse their own AI or analytics that execute outside of the CRM solution. Organizations like Salesforce, IBM, and others have 10 times the number of engineers working on AI than smaller firms, so whichever system you choose should leverage these larger platform-player's capabilities -- not try to compete with them.
Interaction analytics should be native to the contact center and CRM solutions, both through the common desktop and the data integration layer to maximize efficiencies in usage, dashboards, reports, and data mining.
Interaction Analytics: Bringing It All Together
If driving revenue out of new customers and more revenue out of existing customers at a lower cost of sale is important to your organization, you've got to consider the way your customers feel about doing business with you -- Net Promoter Score is not enough. New economics and new business models require new ways of engaging customers and analyzing effectiveness.
Cloud-based contact center solutions take CRM to new levels by creating a turbo-charged engagement platform by which you can effectively, efficiently, and economically engage your customers across their preferred channels. Interaction analytics should augment your CRM and contact center strategy with both descriptive and predictive analytics capabilities -- descriptive so that you can understand what happened in the past, and predictive so you can understand how to best engage customers in the future.
Organizations that can successfully leverage interaction analytics can drive significantly higher levels of customer engagement, leading to increased revenues at lower cost-of-sales than previously possible. Don't compete on price, just make your customers happier and they will keep buying.
