Welcome

Answer 8 simple questions to determine your data strategy & customer analytics maturity compared to your peers

And receive customized results and recommendations based on your responses.

To drive growth and remain competitive, organizations must be data-driven when making marketing decisions and executing their customer engagement strategies. Data and insights professionals must continually evolve their data and analytics practices by obtaining a complete understanding of their capabilities, developing gap assessments for short- and long-term needs, and ensuring that data and insights are properly organized and governed. This evolution provides users across their organization with access to the right data and actionable insights to maximize the value of their customer relationships.

How equipped is your organization to tackle the challenge?

Questions

Does your organization come from an industry that collects a significant amount of 1st party customer data?

(Identifying your industry-type will provide a more robust peer benchmark, along with actionable recommendations.)

Industries that collect a significant amount of first-party customer data include: Financial services and/or insurance, Healthcare, Media and/or leisure (including entertainment), Retail, Telecommunication services, Transportation and logistics, Travel and hospitality (including foodservice/restaurants)
Industries that do NOT collect a significant amount of first-party customer data include: Agriculture and food, Beverages/Alcoholic Beverages, Consumer product goods and/or manufacturing, Consumer services, Electronics and/or consumer technology, Technology and/or technology services

Questions

How well do the following statements describe your organization's customer analytics efforts specific to strategy?

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We are an insights-driven business and customer analytics is an integral part of how we understand customer behavior to sharpen our core value proposition.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

Customer analytics is a coordinated activity across marketing, customer experience, customer service, product management, operations, and all other relevant customer-facing facets of the business.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

My organization relies heavily on customer data insights to support decision-making across all levels of leadership.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

Our customer analytics are focused on assessing both short- and long-term performance outcomes/metrics.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

2 of 7

Questions

How well do the following statements describe your organization's customer analytics efforts specific to structure?

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

Our customer analytics team consists of a multidisciplinary team of analysts, data scientists, data engineers, customer strategists, and representatives from each line of business that consumes customer insights.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We have a hub-and-spoke organizational model for customer analytics, which consists of a centralized team providing analytics governance, program support, and analytical resources assigned to individual lines of business.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We have formalized partnerships with service-level agreements in place across the organization, including with marketing, data, customer service, sales, operations, and product development.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We have fully integrated our customer-level data with multiple forms of analytics (e.g., multichannel attribution, mix modeling, lead scoring, segmentation, etc.).

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

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Questions

How well do the following statements describe your organization’s customer analytics efforts specific to data?

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We use an extensive set of structured customer data sources and unstructured customer data sources for our analysis.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We have a centralized customer data repository with permissions, data descriptions, and an interface to facilitate self-service access across the organization.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We have resources devoted to data understanding and automating data preparation to incorporate and document new sources so they are available to relevant stakeholders across the enterprise.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

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Questions

How well do the following statements describe your organization’s customer analytics efforts specific to analytics?

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We have a closed-loop customer analytics system that learns from new data to continuously improve, rescore customer records in real time, and retrain models when necessary.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

In addition to tracking the ROI of individual customer analytics projects, we measure the holistic ROI of our customer analytics practice in terms of increased revenue from analytics and decreased costs due to operational efficiencies across lines of business.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We deploy advanced models and predictive capabilities to optimize customer outcomes.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

Analytics and data are fully available on-demand to support decision-making.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

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Questions

How well do the following statements describe your organization’s customer analytics efforts specific to process?

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

Analytics planning is determined by a cross-functional team with input from technology, analytics, ROI potential, and business unit team members.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We integrate the results of customer analytics to design and deliver the next-best experience to customers across all touchpoints and at each stage of the customer lifecycle.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

Our analytics capabilities provide insights in a timely manner for the decisions we make.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We have a highly refined ability to translate data insights into business action with speed and agility.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

6 of 7

Questions

How well do the following statements describe your organization’s customer analytics efforts specific to technology?

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We have a suite of customer analytics technologies, including open-source data science tools and customer analytics toolkits as well as customer analytics solutions for businesspeople, and we leverage AI solutions for emerging use cases.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We consume customer analytics output via real-time updates in dashboards, advanced visualization tools, BI platforms, and AI-enhanced analytics solutions.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

Our customer analytics output is dynamic and supported in real time by customer decision management tools that support all inbound and outbound channel touchpoints across marketing, customer service, sales, operations, and product development in a coordinated manner.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

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Questions

How well do the following statements describe your organization’s data strategy efforts specific to data discovery and source?

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We can effectively determine the trustworthiness of the data source and ownership.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We efficiently evaluate and subscribe to third-party data sources.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We can quickly identify data and analytic products to reuse.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We have robust data preparation and governance processes to ensure data quality and integrity and that the data is as useful as possible to end users.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

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Questions

How well do the following statements describe your organization’s data strategy efforts specific to data capture and ingest?

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We proficiently ingest metadata and descriptions for referencing and assign data ownership.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We efficiently capture new transactions, changes, and sources and integrate with existing sources.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We have effectively established services to internal and external data sources and streams.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We can integrate large volumes of structured and unstructured data into a single tool.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

3 of 9

Questions

How well do the following statements describe your organization’s data strategy efforts specific to data curation and modeling?

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We proficiently catalog and interpret data and set data permissions for analytic, data science, and application consumption.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We have established physical, logical, and semantic standards of our data and data models.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We proficiently assess and apply data quality statistics and certifications to data sets, sources, files, and tables.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

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Questions

How well do the following statements describe your organization’s data strategy efforts specific to data transformation and preparation?

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We efficiently cleanse, enrich, and master data using machine learning and subject matter experts (SMEs).

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We effectively perform data hygiene, recoding, formatting, and merging on data sets using ML and SMEs.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We can effectively create proxy data and test data management sources to augment model development and training using ML and automation tools.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We proficiently define the relationships and links for physical, logical, and semantic data models using sector models and references, ML, and SMEs.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

5 of 9

Questions

How well do the following statements describe your organization’s data strategy efforts specific to data testing and training?

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We proficiently define and refine features to build and optimize ML models.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We efficiently confirm performance, biases, and compliance of ML models.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We proficiently define test data sets for unit and AI pipeline testing.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We effectively coordinate and collaborate on AI application testing.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

6 of 9

Questions

How well do the following statements describe your organization’s data strategy efforts specific to data delivery and deployment?

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We efficiently release and manage data products to data science, analytic, and development teams.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We efficiently release and manage ML models.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We efficiently release and manage AI products and applications.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We efficiently release standards, business rules and logic, services and jobs, data intelligence, and stewardship processes.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

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Questions

How well do the following statements describe your organization’s data strategy efforts specific to data execution and action?

1 - Does not describe my organization at all

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We effectively incorporate model performance outputs to determine improvements and optimizations of the model or model pipeline.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We proficiently manage, maintain, and optimize the performance of data processes, jobs, queries, policies, compute, and storage resources.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We proficiently manage and maintain applications, versions, and application performance.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We effectively execute support and processes for governance policy management and compliance.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

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Questions

How well do the following statements describe your organization’s data strategy efforts specific to data observation and evaluation?

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We effectively monitor, track, and alert stakeholders to data anomalies, drift, and unintended outcomes.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We effectively monitor, track, trace, and alert stakeholders to physical and logical data anomalies that lead to policy noncompliance.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We effectively monitor and alert stakeholders to data adherence to governance policies.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

We have dashboards and reporting to achieve and grow business outcomes.

1 - Does not describe my organization at all.

2 - Slightly describes my organization.

3 - Moderately describes my organization.

4 - Mostly describes my organization.

5 - Completely describes my organization.

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Results Overview

To better understand what your maturity result means and to receive recommendations on how to improve your customer analytics practices going forward, please register below.

To better understand what your maturity result means and to receive recommendations on how to improve your data strategy (DS) practices going forward, please register below.

To take their customer strategy to the next level, customer insights professionals must consistently evaluate and improve their data and analytics practices across several fundamental pillars. Our assessment evaluates participants across 6 key disciplines that comprise customer analytics maturity:

Strategy

Establish a culture that values insights-driven decision-making and continuous optimization.

Structure

Create a cross-functional analytics organization that collaborates and coordinates across internal teams and external partnerships.

Data

Strive for high-quality customer data sourcing, management, and preparation.

Analytics

Align your firm’s use of various analytics techniques to key business outcomes.

Process

Define processes for analytics prioritization, execution, activation, and dissemination.

Technology

Implement technologies that will produce, distribute, and activate customer analytics.

Your customer analytics maturity result: Your data strategy maturity result:

Low

Mid

High

Chart Placeholder

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Recommendations

Customer Analytics Maturity Rating: Low

Your score classifies your company’s maturity as “Low” as it relates to its customer analytics (CA) practices, like 38% of companies surveyed. Our study revealed that companies with lower CA maturity are less prepared than their peers to deliver on primary customer analytics objectives such as improving ROI of marketing spend, finding insights based on relevant data, and being able to better address customer needs. Though lagging behind their more advanced counterparts, there is still an opportunity for low-maturity companies to uplevel their customer analytics practice by taking actionable steps.

Recommendations to improve your customer analytics practice:

From a strategy perspective, identify potential analytical projects and prioritize a list based on feasibility and organizational value. Work with senior leaders to gain support and budget. Support from leadership is crucial for long-term success. Identify the low hanging fruit for rapid realization of ROI to establish credibility.
From a structure perspective, foster cross-functional collaboration with the right analytics talent. Perform a readiness exercise to determine if delivered insights can be acted upon. Once confirmed, establish a team to perform analytics or partner with vendors for analytical support.
From a data perspective, create a central repository for customer data that multiple teams can easily access. Link customer data with marketing data to glean richer insights. Don’t be overly concerned about the completeness of an ideal dataset. Work with what you have to get the process started.
From an analytics perspective, apply data science techniques such as machine learning to behavioral customer data to meet business objectives. Evaluate performance by measuring impact on customer outcomes such as acquisition and retention. Calculate ROI at the campaign level by linking it to the customer segment level. Begin to experiment with predictive analytics (e.g., customer propensity modeling).
From a process perspective, understand stakeholders’ business requirements and create a workflow to both accept and respond to their analytics requests. Define metrics, success criteria, and timelines to help prioritize analytical projects. As performance metrics are measured, enable a feedback loop to identify barriers to success and areas for improvement.
From a technology perspective, equip your tech stack with a variety of resources including channel-specific analytics tools, business intelligence tools, and advanced customer analytics solutions. Look for partners that can provide needed capabilities and expertise versus needing to build everything on your own.

Customer Analytics Maturity Rating: Mid

Your score classifies your company’s maturity as “Mid” as it relates to its customer analytics (CA) practices, like 41% of customers surveyed. Our study revealed that only 35% of mid-level CA maturity companies are prepared to improve on their ability to quickly make customer-focused decisions, as compared to 56% of their more advanced maturity counterparts. Being prepared to deliver on customer analytics goals is critical for success. Though mid-level maturity companies have taken steps to action on those priorities, there is still work to be done when it comes to developing a stronger customer analytics practice.

Recommendations to improve you customer analytics practice:

From a strategy perspective, prioritize work that’s informed by previous project outcomes, and bolster analytics initiatives in order to achieve deeper customer personalization. Expand the application of customer analytics beyond marketing use cases. Establish and grow a formal budget for the customer analytics practice. Best practices from other industries can be calibrated appropriately and applied quickly by working with experienced partners.
From a structure perspective, create a solid network of internal and external partners, including data scientists, strategists, and technologists, as well as outsourced service providers. Centralize analytics to share best practices and maintain analytical consistency across lines of business.
From a data perspective, leverage a rich set of internal and external customer data sources to fuel advanced analytics activities. Develop an accessible single source of truth of real-time customer data for analysis. Establish a dashboard or set of management controls for tracking existing data processes to maximize efficiency. Assess gaps in data sources and work with internal experts and/or partners to obtain the most valuable data fields to augment your analytic capabilities and reach.
From an analytics perspective, perspective, implement more advanced techniques such as rigorous ROI calculations of analytics activities and customer lifetime value. Deploy multiple predictive models at scale to achieve precision and accuracy in customer treatments and personalized marketing. Leverage partners who can help your team work faster on existing workstreams or improve depth of expertise. Keep an eye on the market’s horizon line to stay ahead of the competition.
From a process perspective, establish a well-documented process for customer analytics efforts that are aligned with the organization’s strategic planning cycle. Leverage collaboration platforms, business intelligence platforms, and other technology tools to automate processes. Leverage the expertise of partners to help implement new initiatives and approaches, while also continuously improving and evaluating current capabilities.
From a technology perspective, use advanced predictive analytics and machine learning solutions to build and deploy multiple predictive models. Incorporate digital decisioning platforms or real-time interaction management solutions to activate customer analytics in real time.

Customer Analytics Maturity Rating: High

Your score classifies your company’s maturity as “High” as it relates to its customer analytics (CA) practices, like 22% of customers surveyed. Our study revealed that high-level CA maturity companies outpace their lower maturity peers across the board when it comes to being prepared to deliver on their customer analytics objectives including improving customer experience, increasing revenue from existing customers through cross-sell/upsell, and improving the ability to quickly make customer-focused decisions. While your organization’s analytical capabilities may be advanced, there is still plenty of work to be done to maintain a competitive advantage when it comes to your customer analytics practice.

Recommendations to continue to bolster your customer analytics practice:

From a strategy perspective, your customer analytics practice should be a key differentiator and an integral part of your go-to-market strategy. Infuse customer analytics in all customer interactions, from marketing and service, to products and operations. This will foster enterprisewide buy-in and better executive support.
From a structure perspective, create a multidisciplinary team of data scientists, data engineers, business analysts, primary researchers, facilitators, project managers, developers, vendor partners, and other specialists. Continuously improve your teams’ skillset with development and training and equip them with a variety of analytical resources. Leverage partners where possible to support these efforts. Embrace a hub-and-spoke model with a center of excellence and dedicated liaisons for each business unit.
From a data perspective, explore advanced data practices, including automating new data source ingestion and integration, investigating data commercialization opportunities, and classifying unstructured data for advanced analysis using computer vision, natural language processing (NLP), and speech analytics.
From an analytics perspective, implement more advanced machine learning capabilities, such as deep learning and reinforcement learning. Estimate the ROI of future projects based on similarity to past projects for prioritization. Leverage advanced metrics like customer lifetime value across the enterprise to orchestrate delivery of the next best experience.
From a process perspective, establish analytics governance processes to ensure adherence to methodology and implement machine learning operations. Integrate customer insights and analytics into key operational execution systems and enable automation where possible to drive efficiency. Measure the effectiveness of analytics processes using process performance metrics and regular stakeholder surveys.
From a technology perspective, innovate with real-time optimization and interaction management technologies to manage dynamic customer journeys and paths. Invest in AI component technologies, such as computer vision, NLP, and speech analytics.

Recommendations

Data Strategy Maturity Rating: Low

Your score classifies your company’s maturity as “Low” as it relates to its data strategy (DS) practices, like 32% of surveyed respondents. Our study revealed that companies with lower DS maturity are at the start of their journey to deliver on key data initiatives, such as enriching data for better insights and action, providing data to the right users across the organization, or providing faster insights for strategic and operational decision-making. However, the top priority across all companies (regardless of maturity) is to improve the use of data and analytics, so it’s not too late to catch up and firmly establish strong DS practices as a foundational component of your business.

To begin making improvements to your DS practices, consider the following recommendations:

From a strategy perspective, clearly define your data priorities and objectives and identify the metrics that will be used to measure success. Work to ensure your strategy has the executive support needed to gain traction.
From a data sourcing perspective, focus on enabling your business to easily find, collect, and access the data that will create a holistic view of your customers and your business. Look beyond only structured data sources and expand support for semi-structured and unstructured data to allow greater insights for sentiment and interests. Partners can be valuable sources of data, but also look for partners that can help navigate data complexities.
From a data preparation perspective, establish clear data governance objectives that will allow you to be more proactive and responsive with how data is prepared for marketing insights, rather than just reacting to ad hoc requests. Next, look to invest in capabilities and partnerships that improve your ability to classify, label, and certify data for easier governance, faster delivery, and data self-service enablement.
From a data optimization perspective, focus on the intersection of customer data, business activities, and decision-making. Create strategies aligned to data types (e.g., marketing data) in order to identify the best tools and partnerships for optimizing data usage. Focus future strategies on enabling more predictive and prescriptive analysis to drive efficiency.
From a data sharing perspective, ensure you have the right systems in place for robust data reporting and dashboards that allow data to reach the right people within your organization when they need the insights. Data and insights must be enabled for collaboration and data sharing from the start and allow data to move quickly, easily, and at scale across all business systems and teams.

Data Strategy Maturity Rating: Mid

Your score classifies your company’s maturity as “Mid” as it relates to its data strategy (DS) practices, like 44% surveyed respondents. Our study revealed that approximately one-third of respondents, on average, with moderate DS maturity feel very prepared to deliver on key data initiatives, compared to 50% of respondents from organizations with high-DS maturity. Data initiatives include tasks such as connecting the dots on marketing data, integrating structured and unstructured data, providing self-service options to data users, and many others. Your organization is taking strides in the right direction, but there is still work to be done to further bolster your DS practices to drive better outcomes for your business in using data.

To begin making improvements to your DS practices, consider the following recommendations:

From a strategy perspective, look to become a truly data-driven organization by making data insights an essential part of decisioning and process automation. Evaluate where your organization could build or invest in strategic initiatives and build a roadmap that identifies key resources and partners needed to achieve that goal.
From a data sourcing perspective, work continuously to strategically identify and add new data and data types to enhance the modeling and insights you can produce. Invest in more flexible data fabrics to master and govern the complexities of federated data.
From a data preparation perspective, focus on improving data integration with master data definitions and conceptual model references. Explore ways you can embed marketing data and analytics into regular decision-making. Data will be most useful when it can be seamlessly integrated with no code and low-code capabilities within existing business systems and processes.
From a data optimization perspective, consider investing in technologies and vendor partners that can help speed up data operations to engineer data pipelines and services and minimize time-to-develop, time-to-deploy, and time-to-optimize.¹ Partners that specialize in marketing data and use cases can help streamline data usage. These improvements will help drive agility and better enable you to meet stricter service-level agreements.
From a data sharing perspective, transition data engineers out of silos and into scrum teams in order to support more cross-functional use of data across your organization for analytics and operations. Expand self-service and data marketplace platforms to customer-facing teams, in particular markets, to allow faster and easier access to insights.

¹ Source: “Evaluate Your Data And Information Management Maturity,” Forrester Research, Inc., November 11, 2021.

Data Strategy Maturity Rating: High

Your score classifies your company’s maturity as “High” as it relates to its data strategy (DS) practices, like 24% of surveyed respondents. Our study revealed that respondents at organizations with high DS maturity are much better prepared than their peers to deliver on key data initiatives such as providing data access to different users across the organization, creating a 360-degree view of the customer, and improving data quality. Despite your current DS maturity, the top priority across all respondents' companies (regardless of maturity) is to improve the use of data and analytics, so don’t be complacent and keep looking for ways to further improve your DS practices.

Consider the following recommendations as you look to further enhance your DS capabilities:

From a strategy perspective, conduct future state planning to identify new data assets, channels, and infrastructure needed to more directly reach your customers. Strengthen data partner relationships to stay on top of the newest analytics capabilities and technologies.
From a data sourcing perspective, as your business becomes more insights-driven, look to strategically source data through AI and intelligent automation. Consider establishing data communities with partners and third parties to share data for joint value and data commercialization.
From a data preparation perspective, explore ways you can embed marketing data and analytics into regular decision-making on a permissioned basis through enterprise tools. When effective, invest in platforms that: intelligently manage workloads; automate data processes at scale; take advantage of serverless compute; and abstract storage, compute, and state. Partners can support your scale by ensuring data usage and decisions continue to evolve and meet your needs. Align data scientists, data engineers, and data stewards in data transformations and preparation steps to streamline the influx of data. Use DataOps and data governance tools (enabled by machine learning) to set data standards, schemas, and controls.
From a data optimization perspective, as your use cases for AI/machine learning increase, put extra effort into maintaining data quality, and constantly look for ways to innovate. Concentrate on expanding existing data and insights into adjacent business scenarios and use cases, and break business silos by unifying data and insights. Managing scale, agility, and elasticity of large data fabrics is challenging and requires a concerted effort for optimizing data workflows.
From a data sharing perspective, chief data officers and other data architecture leaders should focus data design and development on creating virtual twins of the business for a 360-degree view on B2B, B2C, and B2B2C, rather than just revealing insights.¹ Leverage partners’ relationships to reinforce tools and use cases to promote higher utilization of data tools.

¹ Source: “Evaluate Your Data And Information Management Maturity,” Forrester Research, Inc., November 11, 2021.

Next Steps From Kantar

Ready to get started?

For a deeper look, read the June 2022 Forrester thought leadership paper, “Evolve Your Customer Analytics From Tactical To Transformational,” commissioned by Kantar. This study reveals that companies that evolve their customer analytics maturity achieve long term business benefits such as efficiency, revenue growth, better customer satisfaction and the ability to quickly inform customer decisions to outpace the competition.

Kantar can help you accelerate your customer analytics journey. To learn more, visit Kantar.com, or contact our customer analytics experts directly at DataStrategy-CustomerAnalytics@kantar.com.

Next Steps From Kantar

Ready to get started?

For a deeper look, read the June 2022 Forrester thought leadership paper, “Connect Insights To Action With An Effective Data Strategy,” commissioned by Kantar. This study found that high maturity in data strategy is linked to better customer engagement and growth, by delivering consistent and trusted data to support various applications, insights, and analytics.

Kantar can help you. To learn more, visit Kantar.com, or contact our data strategy experts directly at DataStrategy-CustomerAnalytics@kantar.com.

Methodology And Disclaimer

Methodology And Disclaimers

Methodology

In this study, Forrester conducted an online survey of 738 marketing, analytics, and IT decision-makers at enterprises in the US, Canada, and the UK. Respondents’ organizations were from industries that heavily collect and leverage first-party customer data and primarily represented companies with over $500 million in annual revenue. The study was completed in April 2022.

In this study, Forrester conducted an online survey of 1,044 marketing, analytics, and IT decision-makers at enterprises in the US, Canada, and the UK. The majority of respondents were from organizations across various industries with over $500 million in annual revenue. The study was completed in April 2022.

Disclaimers

Although great care has been taken to ensure the accuracy and completeness of this assessment, Kantar and Forrester are unable to accept any legal responsibility for any actions taken on the basis of the information contained herein.

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