Data Life Cycle in Customer Journey

Data Life Cycle in Customer Experience Journey

When enterprises work on the customer experience, they focus on touchpoints – the single transactional points where a customer interacts with different facets of the business and its various offerings. And it is quite logical as well. These touchpoints represent critical points in the customer life cycle that must be understood and served very well. Data and Technology are the Key enablers to delivering the right and just-in-time experience. Unfortunately, most of our enterprises struggle with disconnected data and not-so-well integrated technology stacks. Even companies that have well-integrated MarTech stacks often struggle with silos of data. As we solve these silos, it is critical that we also understand this concept of Data Life Cycle in Customer Experience Journey. Yes data has it’s own lifecycle!

Understanding every aspect of customer experience and the end-to-end customer journey over a period is vital for businesses. Opting for a data-driven, outside-the-box approach toward customer experience journey helps to put customers at the core of business strategy, thus driving loyalty and revenue. The data generated and sourced from various touchpoints and its analysis can be of different forms. It can be descriptive analytics based on operational data to deducing customer sentiment and behavior from real-time data feeds from all channels and applications. Powered by a range of data analytics tools for operational, streaming, and processing unstructured data, businesses can connect the dots among the key touchpoints to optimize the customer experience journey.

The 4 main types of data that make the Data Life Cycle in Customer Experience Journey are:

  • Intent Data: Collection of behavioral signals from Deep Web that help interpret purchase or renewal intent.
  • Behavioral Data: Data that reveals new insights into the behavior of customers on the web, eCommerce platforms, online games, mobile applications, and IoT.
  • Customer Data: Wide variety of data like demographic, personal information collected by businesses to understand, communicate and engage with customers.
  • Product Usage Data: Data that helps to understand how how-often users interact with your product and their behavior while using the product

Here is a highly recommended article to read about the various technical systems like DMP, CDP and Data Lake to identify what your enterprise needs to connect and use these data types.

Data Life Cycle in Customer Journey

Phase 1: Discovery

Also known as the ‘Reach’ or ‘Awareness’ phase, this phase marks the official beginning of the customer lifecycle. Though it is tough to pinpoint the customer’s exact first contact with the business, it is vital to track the initial touchpoints as accurately as possible to design further marketing and advertising strategies. The data that can be collected during this phase includes the typical search terms, which are bringing people to the business website, the number of new visitors on the website, the point of return of visitors to a website, online reviews, new followers on social media business page, customers’ interaction on social media, data from AdWords and pay-per-click, and the usual prospects and current customer surveys.

Key data from Data Life Cycle in Customer Experience Journey at this stage are:

  • Intent Data is available mostly as anonymous data that is stitched together to make a best guess on the intent of that account or customer. ABM Tools are a very good example in B2B marketing space.
  • Behavioral Data is also available as anonymous data which can be stitched together to a specific anonymous profile.
  • Customer Data is available in case this is an existing customer trying to buy a new product.

Phase 2: Acquisition (Learn/Educate)

The contact initiating phase begins when the company comes on the radar of the prospect. The sole purpose of this phase is to convert the marketing contacts into leads or potential sales contacts. During this phase, it is vital to know the audience and develop messaging strategies according to specific buyers’ persona. The acquisition phase can provide factual data that is easy to analyze. Now, here there are two types of data involved: what and who. What data includes the view sources and referrals, traffic, event, and goal-related data. Who data consists of the business website’s viewers, their landing point after signing up or registering, and how the business website or app is utilized.

Key data from Data Life Cycle in Customer Experience Journey at this stage are:

  • Intent Data is available mostly as known data that is stitched together with behavioral data to make more targeted and personalized experience.
  • Behavioral Data is the best data available at this stage. As the prospect has clearly identified themselves, hence all their web history can be stitched together to understand their journey on your owned media to be more targeted and useful in their journey.

Phase 3: Conversion (Register/Sign up for Trial)

Conversion is the phase where the rubber meets the road. Here the company completes a qualification event when the sale is completed and a prospect is turned into a customer. The pillar on which this phase’s success lies in selling not just the products or services but the relationship. For instance, customers interested in B2B SaaS solutions aren’t merely looking for suppliers but businesses that can become their partners. Here the most critical data is conversion rate, which showcases the percentage of leads that turned into customers. Conversion rate can be tracked in respect to other metrics as well, such as website traffic. Now all prospects will not convert and will abandon the journey. It is the point where the customer lifecycles of such prospects will come to an end. Analyzing the data and finding out what went wrong with these prospects can help tweak the future marketing and strategies.

Key data from Data Life Cycle in Customer Experience Journey at this stage are:

  • Behavioral Data changes from a prospect and marketing behavior to behavior within the product and other customer-facing digital media.
  • Customer Data is officially in play at this stage as we start collecting and processing that data
  • Product Usage Data may be minimally available to inform any recommendations

Phase 4: Support

Once the customer onboarding process is complete and the product utilization is started, it becomes vital to keep all communication lines open if the customer has any issue or query. This is especially critical during the first 90 days because if the customer fails to see or leverage the product or business service’s value, he will likely leave the association. This is what is called churn. In other words, the churn rate is the percentage of existing customers a business is losing and the speed of this loss. To make a customer experience journey rich and seamless, a proactive approach toward support is the need of the hour. This phase involves different data types such as total volume by channel, the average response time, first contact resolution rate, help delay and abandonment rates, and moments of delight. The complete analysis of this consolidated data can help businesses provide their customers the best support experience possible. At this stage product data becomes a major player especially product onboarding and usage data is a critical indicator.

Key data from Data Life Cycle in Customer Experience Journey at this stage are:

  • Behavioral Data and
  • Product Usage Data In product customer behavior data becomes one of the most important but mostly under utilized data at this stage. In most companies, this data lives in silo behind the walls of product teams.
  • Customer Data continues to become mature and can be used to stitch together the best experience to wow a customer.

Phase 5: Expansion

For several companies, upselling and cross-selling are a way of drawing out as much revenue as possible from every client and customer. But this approach can backfire negatively. Instead, the company’s expansion approach should have the goal to help their customers draw out the maximum value of the purchased product or services. This value optimization can be done by creating a customer experience that delivers growing value over a while, developing a natural increase in base-product utilization, a sensible expansion into the additional features and functionalities, and adoption of logical and suitable other products or services of the company. This is the phase where data generated and gathered in the first four stages is consolidated and analyzed to launch an intelligent, insights-driven expansion strategy, one that is designed to deliver the true value. Additional datasets like product data and customer data is of great use at this stage as you identify what is the next best product can offer.

Key data from Data Life Cycle in Customer Experience Journey at this stage are:

  • Behavioral Data
  • Customer Data
  • Product Usage Data

Phase 6: Renew

It would be a bit unfair to call the renewal phase as an individual phase as it is the resultant of a robust customer lifecycle management. Renewals don’t lead a great customer success; rather they are the outcome. If an organization is facing the challenge of lower renewal, it is due to the dissatisfaction in the lifecycle such as issues in onboarding, incomplete adoption of the product, failure of utilizing the complete features that would have brought the desired value to the client, issues with ROI, etc. During this phase, a general customer consensus can be achieved by conducting surveys or through online reviews. Another key performance metric of this phase is churn rate. To sum it up, the data gathered in all the above-mentioned stages, its analysis, and utilization decide the success of the renewal phase. Most importantly, this activity should start few months before the renewal date and not few days before.

Key data from Data Life Cycle in Customer Experience Journey at this stage are:

  • Intent Data is available mostly as known data that is stitched together with behavioral data to make a targeted and personalized experience. Also, data from the deep web can be stitched together to understand if the customer is shopping around and should be offered appropriate offers or solutions to create stickiness.
  • Behavioral Data especially Product Usage Data come together to put together a very customized experience to ensure that the strong value proposition and ROI can be demonstrated to the customer so that they are motivated to renew the service. This data can be combined with Customer Data to offer the best promotions or renewals with no promotions.

Mapping the entire customer experience journey with the right type of data at each phase helps a business understand their customers’ experience and delivery an amazing experience at every touchpoint. To sum up, it is a CONNECTED and COMPREHENSIVE data-driven, outside-in approach to deliver an outstanding, seamless, and rich customer experience that wins both the game of customer experience and business growth. I hope this Data Life Cycle in Customer Experience Journey helps you enable great growth for your enterprise.

You can follow the discussion at https://www.linkedin.com/pulse/data-life-cycle-customer-experience-journey-rohit-prabhakar/

Top Data and Analytics Trends to Watch out for 2021

The year 2020 has been challenging for many companies, but one area that has seen consistent growth even in the uncertain market condition has been data analytics. Businesses now realized the importance of data and analytics; without the right data and proper analysis, it is difficult for a business to make the best decisions. The change in consumer behavior is one of the major factors pushing businesses worldwide to update their business strategies to stay relevant and future-ready consistently. Pandemic has given opportunities for businesses to change their business strategy and prepare them for unexpected market scenarios. This blog will discuss top data and analytics trends that flourish in 2021.

data and analytics

Artificial intelligence

Artificial intelligence is the science that aims to execute tasks performed by the human with the help of machines. With AI development, businesses have changed their way of analyzing data and analytics to make crucial decisions and strategic changes to stay relevant and competitive. It is predicted that businesses will shift from piloting to operationalizing AI by the end of 2024. In this post-covid market scenario, all the historical user and business data will no longer be valid due to changed market conditions. The AI-based algorithm will help businesses in 2021 detect anomalies by learning from historical data and will notify the users immediately in case of an unexpected event.

Cloud 

Cloud will be the future of data storage, and the COVID-19 pandemic resulted in a faster shifting of the businesses towards the cloud. The businesses that failed to adapt to this sudden shift have paid a high cost in 2020. The adoption of hybrid and cloud systems has given the organization the power to involve everyone in their organization in decision-making processes rather than seniors taking all decisions. Cloud services are enabling organizations to work at a fast rate and doing quick and efficient work analysis. 

These enhanced self-service analytics and data literacy applications will play a significant role in making it a reality. This means that the year 2021 will see a shift across the sectors to data freedom and organizational behaviors. There is no doubt that this is going to be future of the storage and will become a new stack

Real-time Data & Analytics

The post-pandemic market scenario resulted in a shift in consumer behavior, and all the older data became irrelevant for the organization. The need for real-time data and analysis became extremely important for an organization to adapt according to the current changing marketing and developing strategies for staying future-ready. In 2021, real-time data and analytics will become a new normal for businesses and everyday people. Accessing critical information quickly and adapting to new challenges that the next year will bring along with it will be the biggest challenge for businesses. There is no doubt that Business Analytics and Business Intelligence Solutions will be more in action in 2021, and real-time data analysis will be their main driver.

Data security

Data security is one of the most discussed topics that remained on everyone’s lips in 2020 and will continue even in 2021. The implementation of privacy regulations in the USA, EU, and other countries have heated the discussions of protection of users’ personal information and data security. In the year 2021, most businesses will shift their focus to data security. Businesses that have set these three things – privacy, safety, and security as their primary goal will have the edge over the competitor in the upcoming year. In the pre and post-pandemic, the incidents of data breaches have increased all over the world. It is understandable why users are concerned and why businesses are investing in security products and services.

Mobile Business Intelligence

The next trend that will continue in 2021, mobile business intelligence, has a lot to offer, but the devices’ impracticality has held them back. One of these devices’ significant drawbacks is that the data is large, and mobile screens are smaller than a computer. It makes it difficult for a user to analyze the information thoroughly. In 2021, professionals plan to recreate the analytical capabilities for mobiles or tablets to enhance the user experience. The market is still growing slowly, but more vendors and BI solutions with the option within their software like mobile dashboard will see a boom looking at the current market conditions.

Companies will soon be switching towards these ultimate solutions looking at the benefits of accessing information, more straightforward data representation, etc., from anywhere and anytime. With mobile BI, organizations can access data in real-time, which means faster response to any business occurrences and more freedom to access the information for anyone who isn’t in the office but need to access it.

Software as a service BI

One of the best business intelligence solutions that have progressed in the last year and will continue to prosper even in 2021 is SaaS. COVID-19 pandemic has introduced a new norm of working remotely for companies, even for those who rely on humans to perform daily tasks. Following the current market trend, the businesses that have opted for SaaS BI gained more flexibility and easy access to the data on the cloud from anywhere with any device. These technologies enable an organization data movement, and easy access from anywhere will become one of the most critical trends in 2021. SaaS BI is the future of business analytics and provides the possibility to perform its analysis for a business. This analysis is available to anyone from any location and can adapt according to the current and future market conditions.

Conclusion

These are the top data and analytics trends that have already dominated the year 2020 and will continue to do so even in 2021. Adapting to these new future trends and planning accordingly will undoubtedly give a business edge over its competitors in the upcoming year. Following a data-driven approach has become a necessity for the business, and all these trends will guide and help businesses to stay relevant and future-ready in 2021.