The Ultimate Strategy Guide to Data-Driven Customer Engagement

In today's attention economy, developing best-in-class data-driven and scalable processes to engage customers along the customer journey is essential for most businesses. This article discusses the key strategies, capabilities, and technologies required to achieve that goal. It also shares a few suggestions on how to start this journey.

At most B2C and B2B businesses, data-driven and tech-enabled customer engagement processes can drive performance improvements typically ranging between 15% and 30% and, in some cases, even more outstanding results.

Data-driven and tech-enabled customer engagement processes can drive significant performance improvement at businesses that need to engage regularly with relatively sizable audiences (i.e., 5,000 people or more). These include companies operating in a wide range of industries like, for example, B2C and B2B services, SaaS, consumer products, healthcare, financial services, e-commerce, and specialty distribution. They tend to be less relevant in a few B2B industries characterized by a concentrated demand and a limited number of potential accounts and individuals to engage (i.e., cosmetic packaging, specialty chemicals, oil & gas pipelines, etc.).

Customer engagement goals and strategies

Enhancing customer engagement, together with improving lead generation, is a strategic goal for many B2C and B2B sales and marketing organizations. Essential performance indicators for customer engagement often include:

• Increasing the conversion rate of qualified leads into customers.
• Expanding the share of wallet at existing customers through greater order frequency, cross-selling, and up-selling.
• Reducing customer attrition rates.
• Enhancing customer advocacy rates.

When targeting a relatively large audience, as is often the case at companies targeting consumers or small and medium-sized businesses, to effectively and efficiently engage their leads and customers, sales and marketing teams need a set of specific capabilities, including:

• Segmenting leads and customers based on their profile attributes, interest and intent, and customer journey stage.
• Creating and delivering compelling messages using multiple media formats and channels with a frequency adequate to achieve a sufficient share-of-mind and, ultimately, the expected goal.
• Understanding which messages are producing the desired outcomes and which ones need further improvement. To be successful, businesses need to create valuable interactions that enhance the customer experience and avoid wasting customers' time.

Managing effective communications also requires telling compelling stories, listening to customers and leads, and interpreting their sentiments at scale.

At B2C companies, customer engagement activities must often coordinate with physical stores and customer service centers. At B2B businesses, they must coordinate and complement one-to-one interactions with sales professionals.

B2C e-commerce companies were pioneers in adopting data-driven customer engagement strategies. They began by personalizing the content of their newsletters using visitors' profile data, which resulted in a significant boost in the ROI of their email marketing activities. As B2C and B2B e-commerce and SaaS businesses continued to proliferate, email marketing evolved adding drip campaigns to newsletters. Drip campaigns are sequences of email messages triggered by specific events meant to achieve particular goals, such as converting free trials into paid customers or reducing customer churn. Optimizing drip campaigns requires not only user profile data but also behavioral data, such as the actions users take on an e-commerce website or within a SaaS application.

With the rise of mobile devices and social media, email drip campaigns evolved into multi-channel workflows including email messages, text and social media messages, mobile notifications, and in-app messages. Finally, to achieve the best results, sales and marketing discovered the power of teamwork, working side-by-side, leveraging the same lead and customer databases, and integrating sales professionals' activities with data-driven customer engagement marketing workflows.

In today's digital, multi-channel, and AI-powered world, achieving effective and scalable customer engagement results requires building data-driven and tech-enabled processes to deliver valuable customer experiences and prepare the organization for embracing business processes and customer experiences enhanced by machine learning and generative AI.

As the original distinction between digital natives and traditional companies is rapidly fading, and all businesses must master digital strategies and capabilities, building data-driven and tech-enabled processes is becoming a priority for more and more companies. Achieving this goal requires adopting modern technologies, re-designing sales and marketing processes to be customer-centric, and investing in upskilling human capital.

For most business leaders and Boards of Directors, ensuring that their companies have the required core capabilities to implement data-driven and tech-enabled customer engagement processes should be a crucial priority.

Required core capabilities

To enhance customer engagement in today's digital landscape, businesses must leverage specific technology-enabled capabilities. Luckily, with the advent of affordable and easy-to-use dedicated SaaS applications and cloud managed services, companies can now implement data-driven and tech-enabled customer engagement processes without requirering large data and software engineering teams.

Over the last decade, technology companies and venture capitalists have invested heavily in developing these tools, making it easier for businesses to pursue their strategies and achieve their business goals. Whether it's lead conversion, cross-selling, churn reduction, or any other related business objective, these tools help companies rapidly deploy the technical capabilities required to engage with customers more effectively and efficiently.

The first step to drive performance improvements through effective customer engagement is to evaluate a business's current capabilities and develop an appropriate strategy to close existing gaps.

Following is our take on the five core capabilities required to implement state-of-the-art data-driven and tech-enabled customer engagement processes.

1. Capturing and unifying trusted customers and leads' profiles

Capturing and unifying data about customers and leads is crucial for developing effective customer engagement processes. This step helps identify individuals and accounts, gauge their interests and intents, determine their stage in the customer journey, and establish a single source of truth for essential business facts.

However, for many businesses, this remains a challenge. As interactions occur across various SaaS and custom applications, data is stored in separate silos with different user identifiers and incomplete profiles, and data regarding important interactions is often missing. Additionally, data governance processes to ensure data integrity are often lacking, negatively impacting decision-making, operational productivity, and customer satisfaction.

Businesses can easily overcome these challenges by adopting a customer data platform to orchestrate and unify data flows across all applications (i.e., marketing websites, e-commerce applications and customer portals, CRM and customer service systems, ERP and billing systems, data warehouses, etc.) and a data lakehouse or warehouse to provide permanent storage and a source of truth for all customer and lead data. This approach enables businesses to achieve a comprehensive and trusted 360 view of their customers and leads and accurately and effectively target specific audiences with contextual and personalized messages and actions.

With the right tools in place, sales and marketing teams can eliminate time-consuming manual data collection and transformation tasks. This frees up their time to develop valuable data strategies, identify valuable customer data, orchestrate automated data flows, and test alternative data activation strategies to generate better and faster business insights and greater customer engagement. By doing so, businesses can deliver greater value to their customers and improve their overall performance.

2. Creating and managing real-time audiences

When it comes to developing automated customer engagement processes, segmenting people into specific audiences using real-time data is a crucial next step.

To create an audience for a specific use case like engaging new leads, targeting existing customers for cross-selling, nurturing prospects that are negotiating a deal with the sales team, or re-engaging dormant customers, sales and marketing team members need to be able to access and leverage all of the relevant data collected and organize by the customer data platform, including:

• People and accounts' attributes such as location, industry, account type, time since their last purchase or signup.
• Actions they did or did not take during a specific period when visiting website applications or mobile apps (i.e., loaded a marketing website page, downloaded a case study, submitted a purchase order, etc.).
• Messages they exchanged with customer service and open support tickets.
• Updates created by the sales team regarding deals in negotiation.
• Order information generated by their ERP system.
• Status regarding product or service deliveries.

Once the criteria for the audience are defined using a simple and intuitive user interface, the application will automatically update all audiences in real time using the data ingested from the customer data platform to determine which people need to be added or removed from each audience.

The best tools will use these audiences to define the people targeted by each customer engagement automated workflow or campaign and also allow these audiences to sync with other external tools such as marketing analytics platforms or digital advertising systems like Google Ads and Facebook Ads.

3. Activating tech-enabled customer engagement workflows

Creating and managing customer engagement workflows is typically done utilizing a customer engagement platform with a drag-and-drop visual workflow editor. Core functionalities offered by the best tools include the following:

• Defining the audience targeted by the workflow. Whenever an individual will enter that audience, they will automatically enter the campaign.
• Structuring the workflow with functionalities like time delays, logic branches, wait times for specific events, and setting up A/B testing.
• Sending messages to people and APIs:
  • Messages to people are the primary step in a customer engagement workflow. They are composed using a message editor and delivered through multiple channels, including email, text and social media, mobile notifications, and in-app messages.
  • Messages to APIs are programmatic instructions sent to other applications to perform other valuable actions like targeting a user with an advertising message delivered on a platform like Google Ads or Facebook Ads, sending a text message through an external service like WhatsApp, delivering a physical postcard or catalog to the targeted user through an ad-hoc service, or automatically creating a task in a CRM system instructing a sales professional to contact a specific person. These are just a few examples, and the ability to send messages to APIs is a compelling functionality that allows sales and marketing professionals to create very sophisticated workflows without requiring support from software engineers.
• Defining conversion goals and exit conditions (i.e., when to remove a person from a campaign).
• Reporting on basic KPIs and trends to monitor the campaign performance.

To build customer engagement workflows that can drive revenue growth, it's not enough to just have a great platform - human expertise is key.

Sales and marketing professionals must have a deep understanding of their leads and customers, develop compelling messages and engagement strategies, and continuously test their approaches to drive improvements in engagement and performance. By focusing on the human factor and making appropriate investments in mentoring and training, businesses can see significant gains in customer engagement and revenue.

4. Asking questions and enriching profiles

Once the basic customer engagement infrastructure is in place, adding the ability to listen to leads and customers by asking them questions and storing the answers in their profiles can help generate much better insights and achieve a greater level of engagement.

By adopting a micro-surveying tool and integrating it with the customer data platform, the messaging platform, and other web applications, sales and marketing teams can quickly introduce the ability to listen to their leads and customers at scale. While the response rate for long surveys and website forms is usually meager, micro-surveys - that is, one or two questions delivered via email or while leads and customers are using a web application or mobile app - have very high response rates.

Micro-surveys are a crucial tool for generating valuable insights (i.e., the channels driving the most qualified leads, the reasons for abandoned purchase orders, the most valuable product or service features, the name of the most significant competitors, the net promoter score, etc.) and using those insights to create more effective and personalized customer engagement workflows can drive substantial performance improvements.

At B2B companies, enrichment platforms can also enhance the efficiency of customer engagement operations. By leveraging these tools, sales and marketing teams can automatically update contact and organization profiles with publicly available information, reducing the need for tedious and error-prone manual data entry tasks. This allows them to focus on more valuable activities, such as developing compelling messages and creating valuable interactions with customers.

5. Leveraging machine learning and generative AI technologies

To develop state-of-the-art, intelligently automated customer engagement processes, integrating machine learning and generative AI technologies is a crucial final step. By leveraging these advanced technologies, businesses can further enhance their customer engagement strategies and achieve even better results.

Many companies already use machine learning to generate valuable predictors about their leads and customers. These predictors can help estimate the quality of a new lead, the product or product category that a customer is most likely to buy next, and a customer churn risk, among other things.

Although the adoption of generative AI technologies has only just begun, its usage to generate more compelling messages is spreading rapidly. However, the ultimate goal in the near future will be to leverage this technology to generate more effective and personalized customer engagement workflows, which will lead to better customer experiences and higher engagement rates.

Recommended technology stack

Developing effective customer engagement processes, leveraging the talent of sales and marketing teams, and driving continuous performance improvements require a proper technology infrastructure in place. Given the vast number of tools available in the market, however, choosing the right ones can take significant time and effort.

For first-time users, many tools may appear to have similar functionalities, and it takes experience to appreciate the small but essential differences between them.

Following is our 2023 recommended technology stack for automated customer engagement at middle-market companies, offering the best mix of functionalities, ease of use, affordability, and customer service.

Customer data platforms

RudderStack and Twilio Segment are our recommended customer data platforms to collect, unify, and activate customer data. They both offer a wide range of essential features, including an extensive library of data connectors with data sources and destinations, a powerful identity resolution engine to unify various user identifiers into a valuable identity graph, functionalities to manipulate and transform data in transit, and comprehensive data governance and compliance features.

Each of these tools is a solid foundation on which to build a customer engagement technology stack and an essential component for creating 360 leads and customer profiles and clean and model-ready data for machine learning and AI applications.

Data lakes, data warehouses, and data lakehouses

A best practice in the industry is to use data lakes and warehouses as a permanent storage and source of truth for all customer data. Given customer data extraordinary value, it is essential for all businesses to eliminate data silos and prevent vendor lock-in with proprietary solutions. All the leading providers of cloud infrastructure services, as well as a few specialized technology companies, offer excellent solutions for this purpose.

For data lakes, we recommend the following products: Amazon S3 Data Lake, Microsoft Azure Data Lake, Google Cloud Storage Data Lake, and Databricks Delta Lake.

When a data warehouse is also needed, we suggest using one of the following leading solutions: Amazon Redshift, Snowflake Data Warehouse, Google BigQuery, Microsoft Azure Synapse or, for smaller applications, PostgreSQL.

An emerging new technical architecture, well suited for middle market companies, is the data lakehouse. By combining the low cost and ease of use of a data lake with the more advance functionalities and performance of a data warehouse, this solution can well support both analytics and AI/ML workloads. All the technology providers listed above offer a data lakehouse solution.

It's worth noting that both RudderStack and Twilio Segment offer full support and integration for all the aforementioned data lake and warehouse solutions out of the box.

Data analytics

To effectively analyze and monitor the performance of customer engagement activities, businesses often require a marketing and product analytics platform along with a business intelligence platform. With these tools they can develop and distribute KPIs, dashboards with various analyses, and generate valuable business insights from the data.

There are numerous tools available in the market for this purpose, but finding the right balance between functionalities and cost is crucial. For marketing and product analytics we recommend Mixpanel and Amplitude. For business intelligence, the solutions we rely on the most are Amazon QuickSight and Metabase.

Customer engagement platforms

Automated customer engagement platforms typically offer two core capabilities: creating real-time audiences and managing customer engagement workflows. The market is flooded with many offerings that may appear similar on the surface but differ significantly in actual capabilities. Among the strongest products worth considering are Braze, Customer.io, Iterable, and Twilio Engage.

In our experience, Customer.io stands out and is our preferred and highly recommended platform, thanks to its excellent balance between functionality and cost, and the best customer success team in the industry.

Video management platforms

In today's highly competitive attention economy, the ability to effectively differentiate customer engagement is crucial. While the content of the messages remains the most critical factor, the ability to use different media formats can significantly enhance the impact of the message. Based on market tests, it has been observed that a combination of text messages and video messages can improve engagement rates significantly, and our go-to video platforms are Wista and Vidyard.

Micro-surveys and enrichment tools

As mentioned before, listening and collecting feedback at scale is a required core capability to develop valuable and delightful customer engagement experiences. Market tests have shown that micro-surveys are the best way to implement those capabilities, and our go-to platforms that integrate perfectly with our customer data platforms of choice are Refiner.io and SatisMeter. In addition, these tools offer great functionalities, affordable prices, and are very easy to use.

For B2B companies, utilizing enrichment platforms that offer access to publicly available information about organizations and their employees through an API service can greatly increase efficiency for both internal and external users. This can help create unique and personalized customer experiences. Our top recommended services for this purpose are Clearbit and Apollo.io.

Machine learning and generative AI

In the context of customer engagement, the two core applications for machine learning are predictive analytics and anomaly detection. Predictive analytics allows businesses to estimate essential predictors for each of their leads and customers (i.e., probability to buy, next best product to buy, probability to churn, etc.) and anomaly detection helps to automatically identify unexpected changes in critical KPIs (i.e., sales by customer or point of sales, leads by marketing channel, delivery times, etc.).

Historically, AWS has been the vendor with the most comprehensive set of high performance and affordable AI and ML services and Amazon SageMaker our preferred solution. However, as more and more companies embrace machine learning, other technology companies are entering this space with very compelling offerings.

As ease of use and integration with the whole technology stack is critical for middle-market companies, we particularly value the new AI/ML services recently introduced by Snowflake and Databricks and by RudderStack and Twilio Segment.

However, it is essential to remember that AI results will only be as good as the data used to train the models.

Generative AI is a scorching topic nowadays, and the art of the possible is evolving rapidly with new services and capabilities becoming available monthly. In automated customer engagement, generative AI is currently used primarily by copywriters to craft more compelling messages and by graphic designers to generate better images. In this context, tools we have successfully used internally and for our client work include ChatGPT, Copy.ai, GrammarlyGO, Jasper.ai, DALL•E 3, Midjourney, and Photoshop Firefly. However, we expect that more applications will soon emerge as the best providers of customer engagement technologies will continue to incorporate new generative AI functionalities within their existing products.

For more on Generative AI, you can also read our recent article Entering the Age of AI: Small Steps You Should Take in 2023 (Part Two).

In terms of licencing fees, the total annual cost for a customer engagement technology stack suitable for a middle-market company usually ranges between $120,000 and $200,000.

How to get started

The greatest challenge to complete a new journey is often taking the first step.

In our experience, developing data-driven and tech-enabled customer engagement capabilities at middle-market companies can take from 12 to 24-30 months. Our recommended approach is to organize the work in three phases according to the following work plan.

Concerning the optimal talent strategy, in our experience, middle-market companies can achieve better and faster results when partnering with an external advisor to help scope and launch the effort and build the internal organization. The external advisor should have strategy, technology, and change management capabilities to help the company:

• Define the most appropriate business goals, customer engagement strategies, and targets.
• Select and deploy the required technology stack.
• Develop, test, and validate an initial set of automated customer engagement campaigns.
• Train sales and marketing team members on designing and leveraging data-driven and technology-enabled customer engagement processes.
• Identify the needs for additional hiring and facilitate the selection of qualified candidates.

After this initial period, with the required technology infrastructure deployed, an adequate organization in place, and a greater level of confidence resulting from early positive results, the company will be ready to drive continuous improvements in customer engagement capabilities and performance.

We have successfuly help businesses develop advanced customer engagement capabilities and lift revenue in a number of industries including B2C and B2B e-commerce and SaaS, B2C and B2B services, manufacturing, and specialty distribution.

Augeo Partners is a boutique firm of senior business and technology leaders with a significant track record in driving revenue growth and operational productivity through strategy, process design, digital technologies, and change management. We partner with management teams and private equity investors to accelerate value creation through a set of proven strategies and playbooks. The time to enhance customer engagement processes to accelerate value creation is now, and Augeo Partners can help.