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Top 5 factors that are slowing down insights-to-action in your organization and how to avoid them

Many organizations place a strong focus on collecting as much data as possible. However, being data-rich is not the same as being insight-rich. While collecting data is important, analyzing it to gain insights is invaluable to maintaining the competitive edge and long-term business success.

Armed with insights, organizations can get quantitative and qualitative answers to business-critical questions that enable sound decision-making with number-driven rationale.

Continuous and sustained business success depends on how quickly and strategically organizations can convert their data into insights, then put them into action. If you aren’t able to leverage insights-to-action, the following five factors might be your culprits:

1. Not Democratizing the Use of Actionable Data

Insight-driven organizations don’t just gather data, they put it to use to create better products, design more effective strategies, and engender a superior customer experience.

In a nutshell, “Data Democratization” refers to hindrance-free, easy access to data for everyone within an organization. Further, all stakeholders should be able to understand this data to expedite decision-making and unearth opportunities for quicker growth.

The distribution of information through Data Democratization enables teams within an organization to gain a competitive advantage by identifying and acting on critical business insights. It also empowers stakeholders at all levels to be accountable for making data-backed decisions.

Concerns that commonly keep organizations from democratizing data include; poor handling and misinterpretation by non-technical teams, which can lead to inept decision-making.

Additionally, with more people having access to business-critical data, the question of maintaining data security and data integrity cannot be ignored. Another concern relates to cleaning up inconsistencies – even in the smallest datasets and files. These may need to be converted into different formats before they can be used.

However, technical innovations – such as cloud storage, software for data visualization, data federation, and self-service BI applications – can make it easy for non-technical people to analyze and interpret data correctly.

Data Democratization is expected to give rise to new business models, help uncover untapped opportunities, and transform the way businesses make data-driven decisions. You don’t want to overlook this!

2. Not Forming a Single View of Customer Data

With organizations using the multichannel customer service approach, customers have the option of using a number of two-way channels to communicate with brands. These typically include email, phone, live chat, social media, online forms, and so on. It, therefore, becomes difficult for customer service teams to unify customer data received from these sources for analysis and interpretation.

Enter Single Customer View (SCV).

SCV enables organizations to track customers and their messages across channels, which in turn, helps with:

  • Unifying customer data on enterprise-wide internal systems and using it meaningfully.
  • Capturing customer activity across channels and devices.
  • Using customer information to engage with them across touchpoints.
  • Enhancing sales figures and improving future customer interactions.
  • Improving customer retention and conversions, as well as enriching customer lifetime value.

United Airlines, upon merging with Continental Airlines in 2012, wanted to integrate the two companies’ websites. United also wanted to ensure that its analytics and marketing pixel tagging was accurate, and ultimately, work towards a single customer view across all channels. They unified tagging across all digital touchpoints, including mobile apps and kiosks.

United managed to combine all customer data, which left them with cleaner datasets, greater consistency across applications, and the elimination of inefficient data silos. They also achieved higher ROI, as well as enhanced analytics and optimization programs that unified customer data and enabled greater mobile marketing agility.

Creating SCV isn’t easy. Some major barriers include:
  • Legacy systems that deter data integration and standardization.
  • Outdated, redundant data that lacks quality and accuracy.
  • Operational and departmental silos that prevent the delivery of seamless customer experiences.

Mentioned below are a few steps organizations can take to overcome these barriers and form a single customer view.

  1. Employ customer journey analytics: This empowers organizations to skim through innumerable complete customer journeys and connect several touchpoints across channels and timelines.
  2. Integrate customer data: This refers to putting together all customer data from different touchpoints – such as data warehouses, POS systems, marketing automation programs, and other data management systems. Customer data includes demographics, web and mobile activities, preferences, sentiments, interactions with customer support teams, social media, transactions, and so on.
  3. Connect data with specific people for customer identity matching: Identifiers that can isolate people who engaged in specific interactions include email address, credit card number, device code, transaction number, cookies, IP addresses, agent ID, salesforce ID, and more.
  4. Empower Your CX Team: CX teams can benefit greatly from accessing real-time customer information to deliver exceptional experiences. Industries that receive unending customer queries (like banking and telecom) can use SCV to resolve them quickly, leading to enhanced customer satisfaction.

3. Reserving Innovation Only for R&D

Frequent technological advancements and industry disruptions have necessitated digital transformation in organizations. This, in turn, has given rise to new opportunities for growth and exchange of innovative ideas that transcend the borders of the R&D department.

If organizations are to encourage an enterprise-wide culture of innovation, they need to redefine metrics and incentives accordingly. New ventures and initiatives cannot be evaluated with traditional metrics to measure success.

Most managers agree that taking calculated risk is crucial to innovation, but putting this thought into practice is easier said than done. Hence, the focus needs to be on encouraging teams to take smart risks. It helps to clearly define a “smart risk” for teams and departments to distinguish the areas where risk is encouraged (and where it isn’t).

Of course, taking smart risks in business involves using advanced data analytics, Internet of Things, images, annotations, RFID, telematics, audits, among others. Every team brings unique perspectives to the table, which can provide ideas and insights to solve business problems. These insights are at the heart of driving successful innovation.

4. Lack of Data Consolidation

If your data is in multiple silos, gaining actionable insights from it can be a mammoth task for your organization. More often than not, the lack of customer insight is the result of the inability to consolidate customer information across channels.

The biggest challenge here is the inconsistent collection of customer information in each channel. For example, a global hotel brand may have collected customer data in a bid to improve customer service. However, because the data was collected from various sources, it resulted in some serious inaccuracies and inconsistencies.

However, after consolidating each customer’s data in one place, hotel staff can provide them with enhanced services and experiences across properties. Staff can guide a yoga-aficionado guest with a list of local studios and class times; or simply stock the mini-bar with their guest’s preferred beverages. Such steps will result in improved customer satisfaction and increased customer lifetime value.

Challenges related to data consolidation can be mitigated by enhancing data collection methods, in terms of accuracy and consistency. This also applies to how and where the information is stored upon being collected.

Organizations will do well to use cloud-based data consolidation tools. These tools are especially designed to provide speed, security, scalability, and flexibility, regardless of the place or in the form in which your data exists. These systems ensure that complete and accurate datasets are available at your disposal at anytime from anywhere.

5. Not Measuring Success on a Customer Level

Modern organizations use multiple channels to connect with and engage customers, but struggle to derive actionable insights from all the available data. It is necessary that organizations gauge quantitative and qualitative data to arrive at measurable and countable answers, which can be converted into numbers and statistical data.

This, in turn, will help decipher customer motivations, indicate their preferences, and highlight the scope for improvement.

Advanced technologies – such as Artificial Intelligence, Machine Learning, Augmented Reality, and Blockchain – are being leveraged to engage customers and provide them with seamless, connected, and hassle-free experiences. These solutions can also measure customer satisfaction using quantitative and qualitative data, which can be gathered through questionnaires and surveys. Combining survey answers and hard data will present the most direct picture of customers’ experiences.

The most crucial elements of success with customer experiences when implementing these technologies are: putting data at the center of your customer experience and seamlessly merging the digital and the physical (i.e. merging data from in-store and online experiences).

It also helps to use data analytics to find meaningful success metrics like revenue per visit, average user duration/average user time on site, cost per acquisition (CPA), and cost per lead (CPL) for gaining real-time feedback. Looking through CRM and lead platforms and working out total conversions for a particular time period can prove helpful.

Once these aspects are taken care of, organizations should be able to find answers to their most burning questions.

Steps to avoid the slowdown of Insights-to-Action in your organization

1. Analyze Data with Business Analytics

Business Analytics helps collect and analyze historical data, then employs predictive analytics and generates visual reports in custom dashboards. Predictive modeling can forecast and prepare businesses for future requirements/obstacles.

Organizations can begin using business analytics by asking measurable, clear, and concise questions. This should be followed by setting realistic measurement priorities, and then collecting and organizing data. The next steps involve the analysis of trends, parallels, disparities, outliers, and finally, interpretation of results.

The primary advantage of harnessing Business Analytics is to decipher patterns in data to gain faster and more accurate insights. Doing so enables organizations to track and act immediately, as well as formulate better and more efficient strategies to drive desired business and customer outcomes.

2. Simplify the Complex with Data Visualization

In any organization, Data Analytics should not be the forte of only data analysts and data scientists. Other stakeholders must also be empowered to make sense of critical data. Proper, user-friendly Data Visualization is the answer when organizations want to process and translate large volumes of datasets into meaningful insights.

Organizations must realize that there is more to Data Visualization than displaying information in a particular format. It also enables the use of visual instructions that guide users to process the material easily, with business-critical insights prominently featured on the top of the visual hierarchy.

Data Visualization also empowers organizations to easily decipher hidden patterns and make sense of the bigger picture in the ocean of data. With more meaningful data at your disposal, you will see improved decision-making (and revenue growth), as well as customer satisfaction and failure-aversion strategies.

So, you need to make Data Visualization a key skill of all data scientists in your organization. The goal is to make every single insight and decision crystal clear for all stakeholders to absorb.

3. Use AI to Close the Gap

Traditionally, organizations resort to historical data, spreadsheets, and business tools to make sense of their data. However, with different variables coming into play and constraints to consider, doing so across multiple channels can become increasingly complex and error-prone.

By bringing AI into the mix, however, management of data has now become quicker and error-free. Organizations can easily analyze their performance across the value chain in real time. With AI-powered operations, businesses can predict elements such as risks and customer behavior, then devise strategies to improve performances and approaches.

AI makes it possible for data-driven organizations to compare performance and trends, as well as analyze every dataset to gain business insights. These can then be turned into actionable plans that enable businesses to optimize their approach to enhance ROI and better meet customer needs.

AI helps to close the gap between insight and action by increasing scale, speed, and efficiency. Organizations can close the gap by analyzing customer data to derive key information, plan how to implement it, then focus on key performance drivers. Once this is done, organizations must track the progress of their plan and manage risks. After this, the desired outcomes can be achieved.

Decision-making fueled by AI can be done proactively, as well as more efficiently and effectively. Business insights can be embedded into predictive models that enhance business outcomes way beyond what was thought possible with traditional approaches.

Traditionally, organizations resort to historical data, spreadsheets, and business tools to make sense of their data. However, with different variables coming into play and constraints to consider, doing so across multiple channels can become increasingly complex and error-prone.

By bringing AI into the mix, however, management of data has now become quicker and error-free. Organizations can easily analyze their performance across the value chain in real time. With AI-powered operations, businesses can predict elements such as risks and customer behavior, then devise strategies to improve performances and approaches.

AI makes it possible for data-driven organizations to compare performance and trends, as well as analyze every dataset to gain business insights. These can then be turned into actionable plans that enable businesses to optimize their approach to enhance ROI and better meet customer needs.

AI helps to close the gap between insight and action by increasing scale, speed, and efficiency. Organizations can close the gap by analyzing customer data to derive key information, plan how to implement it, then focus on key performance drivers. Once this is done, organizations must track the progress of their plan and manage risks. After this, the desired outcomes can be achieved.

Decision-making fueled by AI can be done proactively, as well as more efficiently and effectively. Business insights can be embedded into predictive models that enhance business outcomes way beyond what was thought possible with traditional approaches.

Conclusion

The process of transforming raw data into actionable insights can be daunting. However, doing so is crucial if you want to stay competent and remain ahead of the curve. To successfully lead data-driven initiatives, organizations must overcome the challenges of data accumulation, analysis, and action.

Integrating data sources and leveraging advanced technology for faster and more accurate analyses is imperative. The future belongs to organizations that are driven by data, and only the optimal extraction and application of insights can give rise to the finest business outcomes.