How Can Companies Enhance Their CRM Initiatives with Augmented Analytics?

Article
By
MathCo Team
May 24, 2021 11 minute read

Delivering top-notch consumer service is undoubtedly the priority of every business. In order to ensure that consumers are delighted with products and services on offer, having a robust voice of customer analysis, i.e., a detailed analysis of consumer preferences and feedback is important.

Customer feedback has also proven to be pivotal to operational success and enhancing customer loyalty. A research by Gartner revealed that companies that actively work with voice of customer programs, spend 25% less on customer retention than those that don’t. The study also proved that collating customer feedback also improved upselling and cross-selling success rates by 15-20%. [1]

In enabling the same, augmented analytics can prove to be an especially significant ally.

What is Augmented Analytics?

Augmented Analytics refers to the process of leveraging AI, ML and other pertinent technologies for repetitive tasks and number crunching so that human talent focusses their efforts on data-driven decision making.

Why is Augmented Analytics significant for Customer Relationship Management?

Customer relationship management can be built through a myriad of methods ranging from social media to website-based interactions, to calls/mails, and so on. Tracking metrics like Customer Satisfaction Scores (CSAT) and Net Promoter Scores (NPS) are vital for understanding customer pulse and an integral part of customer relationship management..

CRM initiatives often fail because CRM goals are rarely well-defined, and companies do not efficiently leverage CRM data to derive accurate insights and to cater to relevant customer segments.

This is where augmented analytics proves helpful.

Augmented analytics can help setup sophisticated systems that improve success rates of customer relationship management initiatives, by giving insights pertaining to CSAT or NPS that accurately gauge customer satisfaction, brand perception, understand customer behavior, and fundamentally, prioritizes customers and their preferences, above all.

A leading APAC-based airline, for instance, noticed that their overall success of their customer experience initiatives was impacted by low/negative feedback with respect to baggage handling and crew experience in certain routes. By setting up detailed customer feedback categorization of these interactions, the airline was able to proactively respond to customer grievances. The solutioning helped improve NPS by 10% in a span of 18 months and improve CSAT by 30% in 12 months.

Through this article we discuss the various capabilities enabled by augmented analytics and the challenges that can hinder deriving maximum ROI from augmented analytics-fueled CRM setups.

Augmented Analytics & Consumer-to-Company interactions.

1) Augmented Analytics can help analyze and improve current support channels.

Three in every five consumers shared that “good customer service is key for them to feel loyalty toward a brand.” Studies show that “33% of customers are most frustrated by having to wait on hold. 33% are most frustrated by having to repeat themselves to multiple support reps.

Support channel operations such as call centers can improve efficiency by using analytics to understand incoming call volumes, and help the customer interact with the support team as quickly as possible. Additionally, chatbots can be used to streamline customer troubleshooting, analyze incoming consumer requests to understand key factors, repeating issues, general sentiment across various demographics. This would also help optimize customer pain points like website/app performance, and help address customer queries quickly, while training the models based on customer feedback, thereby improving redressal accuracy and gauging consumer relations.

For instance, a popular APAC-based food delivery application noted that they generated numerous terabytes of data pertaining to customer preferences/purchases week on week. By making the most of AI & ML-powered analytical solutions, they were able to analyze the data and leverage it for enabling delivery efficiency, creating consumer awareness about the choices accessible to them and so on and so forth. The resulting setup has reportedly resulted in a 200% order volume growth. [2]

2) Augmented analytics can help predict service requests and preempt customer churn proactively, thereby bettering customer sentiment.

Channels through which consumers place service requests – through calls, chatbot services, mobile phone applications, mails, etc., are analyzed. Other aspects pertaining to customer journey including purchases, length of customer journey, past requests are cross referenced to segment customers into categories such as ‘likely to churn,’ ‘unlikely to churn.’ Values can be assigned for each customer to prioritize customers that are likely to churn, predict their service requests and address their grievances accordingly so that the customer relations are strengthened.

By setting up a robust, AI-powered customer feedback analysis and prediction framework, a South-East Asian Airline was able to reduce time taken for feedback categorization from 10-12 hours to 1-2 hours. Live report alerts were created the team to identify which complaints were in need of immediate attention, thereby bettering overall customer experience and reducing the possibility of customer churn.

This especially impact profits because studies have proven that “a 2% increase in customer retention is the same to profits as cutting costs by 10%,” and furthermore, “Customers are likely to spend 140% more after a positive experience than customers who report negative experiences.” [3]

3) Create customer profiles that can be used by service associates in call centers/ stores in quickly understanding the customer they are interacting with and provide pinpointed service.

Less than 10% of companies have a 360-degree view of their customers, and only 5% are able to use a 360-degree view to systemically grow their businesses. [4]

In-depth, AI-driven analysis of past interactions, purchase patterns, social media conversations can help different customer support channels to create a holistic image of the customer, allowing the businesses to serve them better and increase retention/satisfaction rates. Quintessentially, this data is dispersed across multiple systems but with augmented analytics these can be combined together by taking a heuristic approach. Also, setting up Customer 360 profiles is also less time-consuming and less effort-intensive for companies who have high analytical maturity.

Augmented Analytics and Company to Consumer interactions:

1) Use social media, blogs, news to understand consumer sentiment/voice of customer.

Voice of customer primarily refers to the customer’s feedback on the product/service that they have purchased, their likes, dislikes and any other relevant information regarding the same. Voice of customer programs result in up to 55% greater client retention. [5]

Having a capable voice of customer program requires businesses to consider different channels of communication between them and their consumers – ranging from social media platforms to websites, blogs, news/advertisement channels. These channels are opportunities for consumers to interact with each other, share product reviews, recommendations, feedback, etc.

By using NLP techniques, data can be mined from these interactions to highlight important consumer mentions/conversations and alert the business on pivotal feedback. This can prove vital in gauging brand image, product success, consumer product preference, and even prove indicative of newer business trends. These efforts that are driven by consumer satisfaction can go a long way in ensuring higher customer retention rates, create alerts for quick conflict resolution and improve customer relations. Additionally, the data can also be utilized to create new customer segments and identify possible groups to target for new product launches, etc.

2) Monitor different trending topics to customize ads and to stay relevant to latest customer preference.

This helps improve brand perception and creates customer stickiness.

According to survey in the United States, 90% internet users reported that they were receiving irrelevant marketing messages and about 44% survey respondents expressed willingness to switch to “brands that did personalization better.”  [6] Therefore, personalization is key priority for firms, along with ensuring that they align with trending topics to ensure that they get to the consumer first, offer customized recommendations and build a lasting relationship.

It is in lieu of the same that leading D2C brands across industries work towards creating extensive customer profiles and parallelly also track trending market topics to ensure that the right product is marketed to the right customer during a new product launch.

3) Lead conversion ML to help cold callers understand possible conversion rates and prioritize customers with higher conversion possibility.

Predictive models can help identify new possible customers and customers who may come back to the product, i.e., models will create models that predict which customer segments are ideal for re-targeting.

Even as sales teams and marketing teams strive to convert prospective leads into customers, multiple factors hinder successful conversion rates. Lack of accurate customer information, a holistic view of customer behavior and preferences would really help pitch products that best resolve their needs. By leveraging machine learning techniques, past data can be used to predict the likelihood of customer conversion. Additionally, AI can help clean up this data and prioritize leads in the descending conversion possibility by basing it on past trends and historical behavior. These efforts can also help operational practices further down the pipeline predict customer churn rates and employ remedial measures for customer retention, propensity modeling, and much more.

An SEA-based casino chain harnessed customer engagement analytics to enable higher patron engagement and returns with over 85% accuracy. Customer engagement tool was able to track patrons live on the casino floor, thereby alerting the hosts of real-time targeting opportunities and understanding which offer could further improve customer retention and long-term patron attendance.

4) Lead conversion ML to help cold callers understand possible conversion rates and prioritize customers with higher conversion possibility.

Predictive models can help identify new possible customers and customers who may come back to the product, i.e., models will create models that predict which customer segments are ideal for re-targeting.

Even as sales teams and marketing teams strive to convert prospective leads into customers, multiple factors hinder successful conversion rates. Lack of accurate customer information, a holistic view of customer behavior and preferences would really help pitch products that best resolve their needs. By leveraging machine learning techniques, past data can be used to predict the likelihood of customer conversion. Additionally, AI can help clean up this data and prioritize leads in the descending conversion possibility by basing it on past trends and historical behavior. These efforts can also help operational practices further down the pipeline predict customer churn rates and employ remedial measures for customer retention, propensity modeling, and much more.

An SEA-based casino chain harnessed customer engagement analytics to enable higher patron engagement and returns with over 85% accuracy. Customer engagement tool was able to track patrons live on the casino floor, thereby alerting the hosts of real-time targeting opportunities and understanding which offer could further improve customer retention and long-term patron attendance.

5) Analyze customer journey through various digital channels and accordingly help digital marketers show tailored products/services and improve customer retention.

Mapping the journey becomes crucial for optimizing and personalizing the customer experience, across channels and across multiple touchpoints. It helps understand the kind of experience that customers are looking for and helps identify gaps between what they aspire for and what they are actually receiving.

Mapping the user journey can result in a potential 3-fold increase in engagement rates and boost conversion rates further. For instance, when user journey on a mobile application is mapped used behavior analytics it can offer insights into opportunities to enhance visitor engagement and user experience, thereby eventually boosting customer engagement.

Conclusion:

Many companies struggle with making the most of their CRM initiatives and there might be a lot of factors behind this shortcoming.

For instance, most CRM implementation initiatives are taken up by executives. This would mean that while the top executives are aware of how CRM initiatives will change current processes, the staff are not completely clued in about the changes in store and the new technology implementation to follow. To ensure that the transition to future CRM set-ups is seamless, the staff also need to be made aware of how the tools can be utilized in their day-to-day efforts to optimize operations. There needs to be accountability to ensure that the software is easily adapted to.

To make the most of their analytics investments and to ensure that such hinderances do not affect a smooth transition to augmented analytics-powered CRM solutions, firms need to plan in advance for their CRM solutions, evangelize stakeholders across the organization and provide any relevant training.

The CRM industry is booming – the global CRM market size which was “valued at USD 40.2 billion in 2019, is expected to expand at a CAGR of 14.2% from 2020 to 2027.[7]” Therefore, numerous companies are adopting CRM solutions to make the most of their customer data. And for firms that are yet to tap into this gold mine, now is the time to allocate budgets for top-notch CRM solutions and provide the best possible service to consumers.

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