Customer experience (CX) influences almost three out of every four purchases. Yet most enterprises struggle to get Customer experience (CX) right. Part of the reason for the poor Customer experience (CX) score logged by many enterprises is their inability to understand what their customers want.

The solution lies in data. World-over, marketers and decision-makers rely on data-driven analytics to deliver a superior Customer experience (CX).

Data-driven analytics comes with its challenges, though. Most enterprises struggle with data deluge, spread, and diversity.

Data grows organically and resides in multiple sources. For instance, customer data comes from apps, POS kiosks, social media, emails, websites, chatbots, and call centres. In an Informatica survey, 55% of CDOs report data from more than 1,000 sources in their organisation.

Businesses that look at data from these sources independently cannot identify patterns. They miss crucial insights and make erroneous decisions. Integrating data from all available channels is a basic prerequisite for effective analytics. But the disconnected and fragmented data across many systems make the data landscape messy. To compound matters, there is a mix of structured and unstructured data. Valuable, relevant data lie scattered amidst tons of useless data. Such a data landscape makes analytics unviable. 

Manual cleansing and connecting data or using piecemeal tools is a slow, tedious, and error-prone route. It leaves the system unstable and the enterprise vulnerable to security lapses. Rather, automated data integration tools help enterprises bring order from chaos. 

Automated data integration is a three-step process of extract, transform, load (ETL) or extract load, transform (ELT). 

First, extract the data from disparate sources and systems. 

Next, cleanse, join, validate, and standardise data into a consistent format. 

Finally, load the transformed data into the destination enterprise data warehouse. The analytics engine accesses the data from such warehouses. 

Such data integration offers the opportunity of improving customer experiences in a big way.

1. Getting a single view of the customer

Today’s customers engage with brands over multiple digital and physical channels. Such engagements leave a complex data trail. 

Data integration creates a single source of truth by bringing all data into one place and in a consistent format. The integration tools direct data to centralised cloud-based repositories. Or it connects different data sources through robust APIs. Either way, it unifies data and offers a single, integrated 360-degree view of the customer.

For instance, a typical customer may leave data across the email software, Google, Hubspot CRM, and transaction logs. No system alone gives a complete picture of the customer’s behaviour or preferences. But integrations offer context and enable connecting the dots. Marketers can create valuable experiences based on such insights. 

Access to integrated and relevant customer data allows marketers to:

• Get insights into how customers perceive the brand and what messaging resonates most. 
• Define customer segments with higher accuracy, and make contextual, relevant targeting.
• Ascertain the lifetime value of each customer or customer segment.

2. Delivering consistent omni-channel experiences

Today’s customers interact with businesses across multiple channels. Data integration ensures a consistent experience across these channels. 

For instance, all touchpoints access these databases and get the same information. When everyone works with the same data, consistency improves. 

Connecting customer, product and usage data offers contextual insights that:

• Streamline issue resolution. Customer support agents can get the data preceding and related to the issue, to identify the crux of the problem and offer a proper solution. 
• Speed up issues resolution. The data integration tools work in real-time and ensure customer service reps have access to up-to-date customer data. This reduces resolution time and improves accuracy, taking customer satisfaction scores northwards.
• Pre-empt customer churn. Marketers can also use integrated data to predict situations where a customer will likely leave, and take pre-emptive actions. Fort instance, they can reach out to price-sensitive customers with a discount for a longer subscription period.

3. Enabling personalised experiences 

Personalisation goes beyond addressing customers by their names. Customers appreciate enterprises that tailor communication, products, and services to their preferences. 

Automated data integration brings together the customer’s interactions across all channels. Businesses identify customer needs and preferences, and get a deep understanding of the customer. 

Marketers can understand: 

• Customer’s choices and preferences, including their likes and dislikes 
• Insights into each customer’s unique journey. Marketers can treat every customer interaction as a data point and add contextual insights. This sets the stage for personalised ads and personalised product recommendations. 
• Behavioural patterns, or why customers behave the way they do. Such insights make predictions on how customers will react to specific situations easy. Marketers can then take proactive positioning for maximum effectiveness. 
• The position of the customer on the buying journey. Understanding the position enables engaging the customer with relevant messages. The marketer may make more effective cross-sell and upsell and better product recommendations.
• Customer sentiments. Consider a customer who browses the website, adds a product to the cart, but leaves without completing the purchase. Integrating data from various touchpoints gives context to the episode. Marketers can launch informed retargeting campaigns to re-engage the customer. For instance, if the customer has gone to and forth to the shipping page, the cart abandonment may be due to shipping options. The marketer can personalise the engagement by offering alternative or free shipping options. 

4. Ensuring data integrity

Data privacy and security have become more important than before. The reasons include customers’ heightened awareness of data rights and strong regulatory mandates. Any shortcomings in privacy or security also erode brand credibility.

Data integration helps maintain integrity and enforce effective data governance and security policies. Integrating and consolidating data identifies the sensitive data. Enterprises can focus on protecting such important data and need not waste money trying to secure junk data. 

Effective data integration enables accurate analytics. Analytical insights enable making informed decisions that boost the customer experience. But not all data integration tools are equal. Many tools support only ad-hoc or partial use cases, and the data scientists hand-code manual connectors to get urgent jobs done. 

Tools such as Informatica deliver robust functionality that automates the data integration process. Informatica’s ELT tools cleanse, standardise, and integrate data from complex data ecosystems. 

Informatica combines disparate first and third-party data and does a thorough job. Data analysts no longer need patchwork and workarounds to complete the task. Businesses get real-time insights and access to all data sources without performance disruptions. The secure and easy-to-use tools deliver unified, consistent data views.

Informatica’s Master Data Management (MDM) solution offers a unified, 360-degree view of each customer. Businesses can use customer insights solutions to collect, integrate, and analyse data and launch personalised engagement strategies.

The latest AI-powered additions take Informatica’s data integration capabilities to new levels. CLAIRE, Informatica’s artificial intelligence engine powers these latest developments. CLAIRE can manage any data type, pattern or complexity across diverse workloads and locations. The simple and flexible consumption-based pricing model adds to the ease of use.

Enterprises that leverage these tools overcome data integration challenges and reap competitive advantages.