Council Post: How New Customer Data Architectures Can Help Transform Businesses

Today, businesses are taking a step forward and using technology like machine learning, statistical analysis, modelling of existing customer data to predict any future behaviour and buying trends. To summarise, customer data is not just powerful as it tells us who customers are but also what they do and want.
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Customers today are interacting with brands on more devices and across different channels. This results in disconnected customer experiences, which impacts the brands desire to drive engagement through hyper-personalisation. These disconnected experiences collect data in silos, making it even more challenging to analyse and generate insights across marketing and business initiatives. Today’s ‘Hybrid Consumers’, according to research from Upland BlueVenn, connect with companies over a variety of about 20 channels on average, and they demand a consistent experience across all of them. Owing to this, 83% of marketers think it is now difficult to combine customer data because so many people have various identities across different platforms. Additionally, almost two-thirds (64%) of marketers think their team lacks the expertise or abilities to properly segment and analyse customer data. Even though 79% of marketers claim to have unified customer profiles, just 35% of consumers believe brands they contact understand their buying needs. 

Today, businesses are taking a step forward and using technology like machine learning, statistical analysis, modelling of existing customer data to predict any future behaviour and buying trends. To summarise, customer data is not just powerful as it tells us who customers are but also what they do and want.

A proper customer data architecture addresses these challenges by developing a 360 degree view of the customer. It unifies data across silos to build a complete customer profile to boost relevant messaging and profitability.

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Key pillars of data architecture

  • Integration of adtech and martech tools

Marketers have sceptical views on advertising technology (adtech) merging with marketing technology (martech). According to Statistica, there are 8,000 martech solutions available and the technology aims at making life easier but only if the right one is invested in. Merging adtech and martech would be an easy journey for the marketers because they have used their investments in the right advertising where the right data is available. This also helps in knowing prospective clients and channels they prefer to receive your message.

Similarly, this blend of technologies can create a single view of the client  thereby allowing marketing teams to create personalised, real time experiences to meet the ever-evolving customer expectations. Focusing on AI and market automation can reduce complexities while simultaneously allowing AI technologies to simplify work processes and tools to make them more efficient. 

  • Identity resolution

An identity resolution is the science of connecting matching identifiers across devices to one profile helping to build an Omni channel view of the consumer to help brands convey applicable messages through the customer journey. It draws necessary data from various devices like connected speakers, smart TVs, watches etc. According to a Cisco report, the number of devices that are connected to certain IP networks is going to increase three times the global population by 2023.  

In such a competitive structure, it is important that brand marketers identify which online and offline device belongs to which customer and why. Every time any consumer connects with a brand, irrespective of the channel, the identifier can include their mails, IP address, phone numbers and a cookie to understand their preferences.

  • Business KPI-based data management

Key performance indicators (KPIs) let companies assess their own capacity for goal-setting and achievement. They are managed using a KPI management platform and frequently used to evaluate customer happiness, employee performance, and overall levels of engagement with any audience the organisation particularly targets. When it comes to financial analysis, there is a post mortem done weekly trying to evaluate the transactional situation—meaning, how much money comes and goes in. This can be done with the help of KPI management tools where a problem can be solved through the week rather than after a month. KPIs also help in gauging employee productivity and better understand the hours worked, amount of revenue generated, overtime and job status.

  • Connected customer experience

A leader usually relates the digital customer experience with the idea of emails, social media platforms, SMS—basically, elements that fill the gap between the brand and audience. However, the real transformation occurs once the marketing departments and contact agencies leverage technologies that are enterprise-based to resolve customer identities and customise messages accordingly.

This is a huge breakthrough for brands as they have been able to send personalised experiences at scale which speak majorly about customer centricity i.e., where customers are given priority. Furthermore, it was expected that this would eventually also result in increased operational effectiveness and marketing productivity.

However, despite great advancements, there are still challenges in getting the data architecture right, for instance:

  •  Data quality

As one is likely to be working with diverse data sources, the data formats must not have duplicate data or missing data resulting in unreliable analysis.

  •  Scaling

The value of big data lies in volume but if the architecture is not designed to scale up, problems such as high costs for infrastructure maintenance and loss of performance can occur—eventually leading to budget breaches.

  •  Security

Big data contains a large volume of sensitive information which, if not safeguarded adequately, could result in significant privacy breaches. It is important for this sensitive data to be encrypted and anonymised. 

The growth of data infrastructure has been significant. Gartner reports how data infrastructure spent USD 66 billion in 2019, consisting of 24% infrastructure software spent. The data architecture must be evolving every day to meet business needs with the promise that it would scale the business tomorrow. There are few examples of some emerging data architecture: 

Data Lakehouse

Data lakehouse is a new type of data architecture that combines a data lake with a data warehouse to address the shortcomings of each separately. 

This system uses inexpensive storage to maintain massive amounts of data in their original formats, much like data lakes. By adding the metadata layer on top of the store, it also gives structure to data and empowers data management tools similar to those found in data warehouses. This allows multiple teams to access the entire company data through a single system for a variety of initiatives—data science, machine learning, and business intelligence.

Data Mesh

With the intention of using data to drive their business, several firms have made investments in central data lakes and data teams. But, they quickly discover that the central data team frequently turns into a bottleneck. Here, a decentralised approach called ‘data mesh’ architecture enables domain teams to do cross-domain data analysis on their own. The domain, the relevant team, and the operational and analytical data together constitute its core. In order to do their own analysis, the domain team consumes operational data and creates analytical data models.

Data fabric

According to Gartner, a data fabric is a design idea that acts as a seamless layer of data and connected activities. An integrated and reusable data fabric supports the creation, deployment, and use of integrated and reusable data across all environments, including hybrid and multi-cloud platforms, by using continuous analytics over existing, discoverable, and inferenced metadata assets. In order to access existing data or promote its consolidation when necessary, data fabric makes use of both human and machine capabilities. It continuously recognises and links data from many applications to find distinctive, commercially significant relationships among the data points available.

To conclude, a new and differentiated framework is required to build customer data architecture and expand the data-centric enterprise as data, analytics, and AI become increasingly integrated into organisations’ daily business to drive real time business outcomes. Data and technology leaders who will adopt this new strategy will put their businesses in a better position to be more responsive to changing customer preferences and become more agile, adaptable, and competitive. As Thomas Redman, the Data Doc, says, “When there is data smoke, there is business fire.”

This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill the form here.

Neeraj Pratap
Neeraj Pratap is a Customer Experience Management & Marketing specialist with more than 27 years of experience in business/marketing consulting, brand building, data-driven strategic marketing, and digital marketing. He has developed and executed connected customer experience strategies, CRM projects, data integration projects, including customer life cycle management, customer segmentation, and customer engagement for clients across different verticals like BFSI, Auto, Consumer Electronics, Retail and Durables, Pharma, and Media.

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