Customer analytics maturity levels and strategic roadmaps for companies falling under different maturity levels

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According to a McKinsey study, companies that make extensive use of customer analytics are more likely to report outperforming their competitors on key performance metrics, whether profit, sales, sales growth, or return on investment

Today businesses have realised that Customer Analytics is a required capability to gain competitive edge in today’s marketplace. The deluge of customer data in recent years has opened up a series of opportunities for businesses to better understand their customers and take the center stage on strategic decision making. However, research says while businesses have pockets of localized Analytics capability, less than 15% of businesses have ingrained Analytics or believe it to be a differentiating capability within their organization.

Businesses are grappling with organizing and structuring abundance of consumer data, struggling to source high quality from multiple touch points and establishing an organizational culture that embraces insight driven decision making. Forrester’s Customer Analytics Maturity Assessment provides businesses a baseline customer analytics maturity level across six critical competencies: strategy, structure, data, analytics, process and technology. According to the Customer Analytics Maturity report, firms can be categorized into three maturity levels — beginner, intermediate and advanced.

Beginners: Firms that sometimes use analytics for rear-view-mirror reporting based on aggregated data, but do not use it to gather and analyse useful customer information falls under the beginner category. These firms do not invest in dedicated resources to crunch consumer data and infer valuable insights, instead decisions are taken unscientifically based on assumptions and hunches. For these firms, data is available in silos and data extraction sources are not stitched together to create a single view of the customer. Such firms embrace free analytics tools or partially integrated martech platforms to manage and organise data.

Intermediate: These are firms which are in the process of transitioning from reporting to insight and action-focused analytics. These firms will have dedicated resources or teams (analytics specialist or data scientist) who are learning to translate data and insights into action. Teams strive to create a data driven culture that empowers collaborative decision making based on consumer insights and intelligence. Data sources are stitched together focusing towards creating a single view of customers and also facilitating strong attribution capabilities.

Advanced: Advanced companies are utilizing analytics to unlock consumer intent signals and sentiments in real time to deliver meaningful and personalized experiences across multiple touchpoints. These firms embrace AI-driven analytics and insights focused towards delivering significant ROI and CX improvements. These firms will have teams of data analysts and data scientists working together with business teams and collaboratively focusing on delivering the next best experience to consumers. Teams are able to deal with unstructured data and seamlessly push audience data to integrated martech and business tech platforms. These firms generally embrace fully integrated experience platform, including marketing cloud.

Below mentioned are the strategic roadmaps for firms that fall into different maturity levels.

Once teams have assessed their current analytics maturity and identified the strategic roadmap, the next task is to identify the key drivers to achieve customer analytics success. For every firm to progress their maturity level to advanced, teams must strive to establish the capabilities which are imperative to achieve key business objectives. These capabilities will not only drive improvements in marketing, but also feed meaningful insights to customer service, onboarding, retention and other functional teams.

Strategic Approach to Customer Analytics Success

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  1. Ability to Create Single View of Customer:

The mantra for data driven businesses has been the importance of customer intelligence and actionable insights. In the age of customer experience, leading businesses ensure they have maximum visibility of their customers and prospects that they’re engaging with, across a full range of different touchpoints and devices. This becomes the cornerstone of customer intelligence that caters to the requirements of multiple cross functional teams. An ability to create a single view of customers empowers several business functions to take the most appropriate decision at the right time and place to deliver an enhanced and seamless customer experience.

Customer Analytics must be looked at a holistic level considering consumer sentiment and behaviour tracked across owned, paid and earned media. Consumer intent signals captured from queries on search engines can be passed on to an unified customer data platform, which can be further pushed to a campaign workflow to trigger a tailor made direct channel campaign on real time. In fact capturing intent level data can even trigger front line staff to communicate relevant information to consumers at various offline touchpoints and foster impressive outcomes.

This will furthermore lead the consumers to respond in a specific way and that data must be passed back again to the unified data platform in order to feed more behavioral insights to that particular consumer’s purchase funnel. Creating a single view of customers will not only embrace real time delivery of contextual experiences but will also allow analytics platforms to keep learning, optimizing various underlying processes and recommend prescriptive insights to relevant stakeholders.

2. Ability to carry out Predictive and Prescriptive Analytics:

Driving real time decisioning and triggering communication events is not only possible but increasingly expected as a foundational component of great customer experience. Another capability that is underpinned by a complete view of customer interactions and effective use of technology, is the ability to carry out predictive and prescriptive analytics.

Predictive analytics helps businesses understand the most likely future scenario and its implications based on historical consumer data. Predictive analytics can demonstrate what might happen in a ‘what if’ scenario, allowing businesses to predict which changes in any given process are most likely to win or fail. One of the most successful use cases of utilizing predictive analytics in the area of marketing is Lead Scoring. Marketers often leverage the capability of scoring prospects on data management platforms or customer data platforms. This enables brands to deliver differentiated treatments to different categories of prospects — hot lead, warm lead or cold lead.

Prescriptive analytics harnesses AI and ML empowering businesses to learn from consumer insights and prescribe actions automatically throughout the consumer journey. Use cases of prescriptive analytics can be found in personalisation, audience segmentation, content management etc. Adobe Test and Target uses prescriptive analytics to recommend the next best experience across owned assets to the consumer based on historical user behaviour.

3. Ability to Establish Attribution Models:

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With the emergence of power systems of insight, especially in the area of automated marketing, it is imperative for marketers and analysts to establish models that determine how credit for any form of conversions is assigned to touchpoint in conversion paths. It is critical for businesses to understand which channels contributed to what extent to deliver a particular conversion. Businesses can experience significant conversion rates from traffic originating from paid search or natural search results. But does that mean the display ad campaigns which were shown to consumers at multiple frequencies didn’t contribute to overall conversions.

In most lead generation or customer acquisition cases, there are processes which involve assistance channels to bring back the dropped off visitors to the buying journey such as call center, triggered emails, sms etc. In such scenarios it becomes crucial for marketers to consider multi channel funnels to compare how different attribution models impact the valuation of different touchpoints. In order to compare the impact of offline and online channels on driving online conversions, businesses must realise the significance of platform integration and automation.

The ability to capture consumer moments and trigger actions based on analytics generated consumer intelligence depends very much on how internal applications and martech platforms are stitched together enabling ingestion of offline data seamlessly.


Strategic Approach to Advance Your Customer Analytics Maturity was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.