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/

Power lies in Product Adoption

Power of system is realized when users adopts the system. Number of subscriptions or units sold is a great metric to calculate revenue and initial success of a product but the true to success of the product is based on how well it is adopted. Once a product is well adopted, it confirms all aspects of its success like product objectives, customer impact, profitability, market share etc. As product managers we must focus on adoption as the key goal/OKR besides the normal financial metrics.

Most importantly, adoption doesn’t means it has to be a fancy solution. On a product demo from a vendor, I learnt that the most adopted feature of their financial planning tool for CFOs and their financial teams; is the ability to generate data loaded cubes in excel. Who will believe that in today’s world, but that is what finance teams want. Providing the solution that the customers need and continuous improvement is the key to product adoption.

Modern CMO – Leading Growth Marketing

Today’s CMO is just not responsible for the brand, PR, and communications. Two most important goals for modern CMO are responsible for growth marketing with sales and customer experience (CX). In the last few years, you must have seen the rise of demand centers and CX responsibilities in CMO organization. This is the reality of modern CMO who is leading growth marketing.

Three key enablers for every modern CMO leading growth marketing are:

  • Customer Focus
  • Marketing Technology
  • Agility
Lead Modern Marketing with customer focus, martech and agility
Lead Modern Marketing

Customer Focus

The customer is now in control of when, where and how they engage. More complexity arises as a customer uses many devices, apps, channels to engage. Modern CMO wants their team to increase engagement and reduc churn rates. This is not possible without knowing your customer well.

You may be able to get high traffic using good content strategies and investing in paid media. That traffic many times doesn’t convert into meaningful traffic that your marketing and sales team can act on. Once you understand customer needs and mindset you can improve engagement and task completion rates. This will lead to more quality marketing qualified and eventually sales qualified leads.

Customer focus also builds a culture that focuses on improving CX and not just campaigns. This enables your team to think beyond lead generation and focus on customer lifecycle. A great customer experience again leads to business growth. Studies from various groups have proven the impact of improvement in CX to growth in many industries. This also leads to big-time improvement in customer retention. Every customer churn eliminated is more powerful then a new customer created.

Marketing Technology (MarTech)

The secret to effective marketing is good data, meaningful insights and ability to execute and optimize. Right marketing technology stack will enables that vision. A right MarTech stack enables your teams with the power of data, ability to know your customer and automate marketing processes so that your teams can focus on right tasks. Here is a very good graphic on how marketers use Marketing Technology.

Reasons to use MarTech - Know your customer using power of data and automate marketing
Reasons for Use of Marketing Technologies


CMO must think like a CIO and hire leaders that can get the best ROI from their MarTech investments.

The goal of MarTech is enabling the marketer to run their campaigns in the shortest possible time. That means as less development and releases as possible. A right MarTech stack should enable the goal of zero development platform.

Side note: I firmly believe that marketing teams should not have development responsibilities in their teams. In industry terms, this is called shadow IT. All CMOS should have strong relationships with their CIOs and use their teams when it comes to architecture, development, security, and support. Another very good topic to consider in your discussions with CIO is DevOps for Marketing.

Agility

Improving marketing efficiency and effectiveness is every CMO’s goal. No longer we can afford programs that run for months and years. Imagine you are investing millions of dollars on ad spend and end of the year you find that there was only 5-15% ROI.

It is important to change campaigns on short timelines based on the results they are driving. MarTech alone cannot be effective until both your people and processes are agile. Agility will enable your teams to track, analyze and optimize those programs. This will create a culture in your team to plan and adjust all campaigns on a bi-weekly basis. Imagine the cost savings and high ROI that you can achieve with this ability.

Agility is not just forming agile teams, kanban boards, backlogs, and bi-weekly releases. The most important part of agility is to enable your teams with decision-making rights. The biggest killer of speed is organizational hierarchies.

This is nothing new as your product and tech teams have been agile for years. A modern CMO leading growth marketing must connect and learn how their CIO and product team who has already adopted agility.