Improving productivity in customer service with Salesforce

Software tools are steadily improving overall productivity and customer satisfaction by automating customer service tasks.

Already, around two-thirds of customer service professionals report having systems in place that can automate routine case tasks such as information gathering, feedback prompts, classification and routing. Together, automated systems hasten resolution times by collecting case details and assigning workloads. In particular, tools like Salesforce Service Cloud unify customer interaction data within a single dashboard, aggregate omnichannel enquiries and manage complex data workflows via macros.

Until now, telephony systems have been the hardest to automate. Spoken language is one of the richest data sources available, making timely analysis incredibly complex and expensive. Real-time voice data is unpredictable, making processing extremely difficult, as speakers can mix tonality, tense and sentence structure to create ambiguities that computers struggle to process.

Thanks to recent advances in language software development, computer accuracy is now greater than ever. Speech recognition metrics have grown slowly since the 1970s, and Salesforce’s Service Cloud Voice is now 96% accurate. As a result, the latest features from Service Cloud Voice go even further in increasing agent productivity.

Service Cloud Voice automatically records and transcribes call content, tagging customer cases with relevant topic descriptions and information. Cloud-based AI tools then analyse voice data and provide prompts to agents on helpful next steps and potential resolutions. Agents no longer have to focus on taking notes or finding files as everything they need is available in a single dashboard and in real-time via Einstein and Lightning flows.

Service Cloud also stores analytics data for review and quality assurance purposes, helping to shape the future strategies of call centre resource planning. Service leaders can track call lead times and cases frequencies related to products or contracts, as well as how resource availability changes with peaks in call volumes.

However, existing software features are only scratching the surface of what automation tools are capable of.

Service Cloud Accelerator for Retail

Following Salesforce’s best practices for Service Cloud, makepositive’s Service Cloud Accelerator is a fully featured ‘model driven’ solution that reduces time to value, cost and risk for both existing and new Service Cloud implementations.

To help retail organisations address shortfalls within the present-day customer service model, we have further developed our Service Cloud Accelerator with preconfigured, domain-specific components and templates. These components are aimed at helping contact centres in retail deliver tailored customer support and proactive retention strategies. Combined, they will allow retail organisations to keep pace with the future of the retail industry and stand as a leader in customer satisfaction.

1. Streamlining resolutions with integrated data sources

Consumers frequently overlook FAQs and online dispatch updates. As a result, contact centres are overrun with routine questions concerning delivery dates and logistics, with ‘WISMO’ and ‘WISMR’ (‘Where is my order/return?) terms emerging to describe such cases.

Routine cases like these are particularly burdensome for service agents because of their volume and dependence on external courier organisations. Each time a customer calls to raise WISMO cases, service agents are forced to increase case resolution times while they track down order information across delivery partners. Meanwhile, customers face unspecified and ever-growing wait times. According to ContactBabel, customers are now on hold for an average of 106 seconds, an increase of 563% since 2004.

Our Accelerator for retail contact centres utilises Service Cloud Voice and modified App Exchange tools to create a telephony-based FAQ system. By integrating data from over 100 logistics partners including Parcelforce, DHL, Royal Mail and more, you can automate the data discovery process and collect customer-specific order information more quickly.

The result is a more streamlined process for both parties. When a customer queries an order status, agents can automatically see the last three purchases the customer has made and select the specific one the customer is concerned with. From here, service teams can provide updates and address any subsequent matters that customers mention afterwards.

As a result, resolutions for WISMO case types are faster and more productive, enhancing the customer experience and agent productivity. Given the speed of the data discovery, customers will leave the interaction with a highly positive brand impression and a unique feeling of support, which will invite more investment in your brand at later stages.

2. Diffusing customer frustrations via historical analyses

Our research also highlighted high rates of attrition in contact centres. Reviews by Contact Babel shows that UK contact centres have a staff turnover rate of 26% — nearly double the national average of 15%. Recent events are compounding this problem further, creating an even more challenging work environment for staff: 80% of service professionals reported customers are more anxious, 75% were reported to be more demanding and 66% more difficult to satisfy.

High turnover rates are extremely damaging. Recruitment and training are costly and time-consuming and standards can easily slip between generations of employees. Yet, service leaders are nonetheless forced to aggressively onboard new agents before workload pressures create a self-perpetuating attrition cycle. It is therefore clear that data-based tools are needed to improve the agent experience and reduce stress levels, staff churn and recruitment overheads.

With this in mind, we have found ways to equip service agents with tools to navigate sensitive customer cases more readily. We have developed software that uses historical interaction data to generate real-time insights into live customer temperaments. Our software model predicts customer temperaments using a variety of historical data sources, including:

• Service performance: Our model considers what a customer’s experience has been by measuring recent order data against your ideal service tolerances. Where a customer has met or surpassed your service tolerance limits, our software alerts staff before they address the case.
• Number of previous calls: Service agents are also briefed if a customer has called in numerous times over a small period of time and on what recent topics they have raised. Doing so helps workers address recurring customer issues and find permanent solutions.
• Previous performance review scores: Service teams also have access to previous reviews that a customer has left, helping them understand what leaves an impression on them positively or negatively.

Collectively, our Service Cloud Voice analysis tools help staff preempt negative customer interactions and reinforce existing positive ones. By providing your staff with historical analytics, they can navigate customer interactions with more confidence and insight. In turn, they can carry out their responsibilities more effectively and sustainably. Crucially, you can escalate customers with recurring or high-priority cases and defuse temperaments at more manageable stages.

As a result, customer interactions no longer feel intimidating, as service professionals can spend more time applying solutions and closing cases, rather than disarming disgruntled customers. Customers also benefit from faster resolution times and more capable staff, improving overall satisfaction and creating mutually-beneficial interactions within your organisation.

3. Proactive multi-channel customer outreach

Along with convenience and choice, review platforms have become another source of consumer power in today’s retail market. 53% of consumers say they always research before making a purchase, even reading as many as six reviews before purchasing. The practice is most popular with younger, smartphone-owning demographics, which will only make reviews an increasingly important conversion mechanism in future.

According to Trustpilot data, unprompted, organic reviews are caused by emotional customer experiences (positive or negative). Their data shows that problems with delivery are a common factor behind many poor reviews. However, the same analysis found that businesses that replied to their bad reviews were among the platform’s highest-rated, meaning that customer relationships are salvageable even after negative reviews.

Our Service Cloud Accelerator makes monitoring review platforms easier than ever for eager service teams. We have expanded the multi-channel routing features on Service Cloud, allowing your service team to monitor multiple customer review websites and reach out to displeased customers automatically.

Using our smart routing models, you can address unhappy customers before they give future customers reason to hesitate from purchasing. Instead, our Accelerator offers you the opportunity to recover your reputation and recoup lost repeat business. The same tools can also be used to identify systemic performance issues in staff and begin targeted agent training to alleviate recurring issues.

Moreover, your platform activity is visible to future readers who are conducting research on potential purchases of their own. Your activity can provide reassurance to new customers who are keen to find supportive retailers and attentive after-purchase support.

 

Get in touch with us at [email protected] to discuss how you can revolutionise customer service, increasing employee and customer satisfaction.