The biggest and most successful consumer companies have taken a lead in delivering exceptional customer experiences (CX) across different stages of the buying journey. They have honed their CX prowess by building customer data platforms to identify salient customer pain points and resolve those with the biggest pay offs. A customer data platform plays a pivotal role in aggregating data from multiple customer touchpoints in real-time and structuring them to build customer profiles including their most pressing concerns.

The results produced by customer data platforms (CDP) are only as good as the data flowing into them. A growing number of companies are therefore sparing no efforts to aggregate data through Voice of Customer (VoC) programs in addition to aggregating data from traditional sources. These platforms play a pivotal role in the evolution of customer experience design and our understanding of digital journeys.

According to research from Gartner, 75% of large B2B organizations and 65% of large B2C organizations are in the beginning stages of CX maturity. Our experience with consumer enterprises tells us that organizations looking to transform their CX have requirements ranging from reducing everyday customer engagement friction to completely overhauling their strategy.

Regardless of the CX maturity stage at which companies may find themselves in, building a customer data platform is subject to the following considerations.

  1. Invest Judiciously

    As a customer focused company, you may have already invested in a CRM and systems that help you identify your customers, their buying behavior, and the way they engage with you. However, the increasing influence of the digital medium renders them inadequate to track their sentiments. No amount of brand positioning and smart pricing strategies can make up for the inability to identify and resolve customer concerns.

    Due attention should be given to the existing technology stack to build a data platform that complements it and maximizes your investments. For instance, you may have a variety of structured and unstructured data that needs to be aggregated and processed to derive insights. We handled one such situation where our client, a global media company, wanted a data and analytics (D&A) solution to improve ad placements. Their existing solution could not accurately correlate viewer behavior with their ad placement and targeting parameters. Our D&A solution captures viewer behavior in real time and provides timely insights on viewer consumption across channels. It has enabled their marketing department to effectively deliver ads and grow their ad revenue by millions of dollars.

  2. Focus on Experience

    While it is good to have a customer data platform that ingests data from various sources to give you a complete picture of your customers including drilldown capabilities, it is better if it can take further action. You should aim to build a customer data and experience platform that features AI-driven automation, real-time analytics, and UX optimization. Such a platform enables marketers to create compelling customer experiences with simplified workflows for better productivity. It provides meaningful analysis of marketing initiatives across channels.

    A customer data and experience platform is especially useful for creating a seamless omnichannel experience while conforming to regulations like CCPA and GDPR. It secures personally identifiable data and frees customers from adding their personal details thereby reducing cart abandonment.

    We helped a global publishing and event management company to build a customer data platform to collate, curate and manage audience data, B2B products and events data. With all this data in one place, the company successfully doubled its user base by enhancing digital audience experiences with personalized product search and recommendations.

  3. Ensure Personalization

    Technology giants like Netflix and Amazon are leading the way in personalizing their CX with high quality recommendations. Ensure that your data platform has a powerful recommendation engine that identifies patterns in your user data to personalize CX. It will go a long way towards attracting high quality traffic, improving customer satisfaction, and pushing the average order value higher.

    A luxury retail company that approached us to revamp their legacy e-commerce platform benefited from the recommendation engine we built for them. Our AI and ML based platform that analyzes clicks, impressions and historic purchases and features photos of recommended offers based on real-time preferences, increased our client’s sales by 18% in one year.

    Infocepts Foundational data platform solution helps to assess the need for customer data platform, assist in selecting the right tools, define and implement a modern customer data platform using proven methodologies and accelerators.

Get Started with us to build a robust customer data platform tailored to your needs.

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In the rapidly evolving world view, there is a need for new forecasting techniques that are resilient and flexible. The change is required more than ever before since, in the current times, an incorrect decision in any of the domains such as logistics, production, inventory, and retail, to name a few, can pose an existential question to any organization.

Challenges with conventional forecasting models

Any brand throughout its business journey makes decisions to its benefit based on the guiding principles of past data. It is well experienced, as the COVID-19 situation unfolded throughout the world, it not only affected normal life, but also changed the behaviour and psychology of humans. Some changes in consumer behavior like shift to online platforms are more permanent than changes like focused spending. These changes were so quick, that businesses which relied only on the past, saw great variance between forecasts and the actuals. In such scenarios, no concrete decisions can be made based on these models as it would have far reaching impact on the top line as well as bottom line of the brand. The common reasons for most of the forecasting models to fail are lack of indicators to factor in sudden events, heavy dependency on earlier sales patterns, confined seasonal patterns and so on. To redefine this conventional approach, it is necessary to account the variables linked with these changes to provide better insights into changing trends with a reasonable accuracy and lead time.

The change indicators

Leading and lagging indicators are types of technical indicators that either give an indication of what could happen next within the markets or provide information on what has already happened. Leading and lagging refers to whether the indicator moves before or after another metric, such as price action.

To identify the correct indicators for your business, asking the right set of questions is the key. Do financial, economic or un-conventional indicators emerge as potential business influencers?, Here are some common indicators and their influence area –

Financial Indicators indicate trends in the consumer buying capacity while economic Indicators indicate industrial production rate and how the market is constantly changing. Un-conventional indicators like google mobility index indicate people movement around recreational centers, shops, medical facilities etc. While economic indicators are no-brainers, they lack localization and are typically published after-the-fact whereas indicators like Google mobility are localized and available frequently. Hence considering a right mix of both ensures quick learning and implementation mechanism.

For many indicators, the relationship with sales is dynamic and evolving due to the wavering market conditions. Investigating the trends of these lagging and leading indicators brings out fascinating insights about their true nature and cause of the change in pattern.

What indicators should you consider?

Leading indicators may be able to follow the current market dynamics rapidly and provide foresight in business well in advance. Hence identifying and using the relevant leading Indicators for business forecasting in real-time can significantly improve the model’s predictive power and can provide crucial accuracy gains. These indicators should be well supported by business justifications and not based on spurious statistical correlation.

Real-life examples:

For example, Airline passengers can be an indicator of luxury product sales. As more people visit airports, more is the footfall for the hub of luxury brand stores, leading eventually to a higher demand for these products. There is a time gap between the booking of the airlines and the sales, and therefore, the airline passenger could be a useful leading indicator.

Another example would be lockdown index as an indicator of luxury product sales. As the lockdown index decreases, the number of people traveling increases, leading to higher demand for products at stores. Again, there is a time gap between lockdown index relaxation and travel plan execution, providing enough time for the companies to prepare for upcoming surge in demand.

Practical implementation of such a non-conventional model for a brand retailer, shows some fascinating correlations like increase in stock market’s S&P 500 performance in the past three weeks positively impacts its current sales while increase in lock down index in the past two weeks decreases the current sales. Similarly google mobility indicators like percentage change from baseline in movements amongst parks, grocery stores, and pharmacies also impacts sales.

To identify these leading indicators, one should study correlation pattern between sales and various time lags of these indicators. As this relationship rapidly changes with time, these observed indicators will constantly influence sales showcasing their highly predictive nature. Once the right indicators are identified, data availability could be the next challenge. Data availability challenges can be more often resolved using proper data mining, modelling and feature engineering techniques.

What are the trade-offs:

A trade-off between business guidance and correct statistical models can create desirable forecast with added confidence using model evaluation metrics like accuracy or mean absolute percentage error (MAPE). Creating a transparent framework can offer significant business insights for managerial adjustments to the forecasts and the eventual acceptance of the forecasts in the organization. Since the data required for this type of modelling needs to be recent and real time, the practical usefulness of this non-traditional forecasting framework is limited to shorter horizons. The framework has the potential to impact the business positively given traditional models may fail to cope with the situations arising in uncertain times like COVID pandemic. The accurate predictions will drive in-time decision making that may lead to better product availability and higher customer satisfaction.

To build a forecasting model that may impact your business positively, be observant and identify the events, triggers or things impacting for your business. Analyse your position in market and changing sales pattern within your consumer behaviour.

Infocepts specialises in solving a variety of data science problems using techniques like Predictive Analytics, Forecasting, Cluster Analysis, NLP, Recommendation Engines, Computer Vision and many more.

Get in touch to know more!

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Using HoloLens 2 technology allows for real-time data analytics in retail stores

Infocepts collaborated with Neumont College of Computer Science to implement Power BI reporting and functionality with Microsoft’s HoloLens 2, allowing users to not only see data on products but also see the product in the HoloLens world.

We interviewed the team behind the project to seek answers on the complex matter of using real-time data to make informed decisions in retail stores.

Q: What motivations do retail stores have to embrace mixed reality for analytics?

Analyzing data on products is time-consuming, unengaging, and inefficient. Retail store managers sit down at a desk and plan out the store without actually being able to see the store or products. They can see the analytics but the actual store layout, feel, and atmosphere is lost when they are distanced from it, making planning from an office that much more inefficient. Workers have to place on the products without much help other than paper layouts, making product placement difficult and time-consuming.

Q: How can a HoloLens 2 app help?

Infocepts developed an application to streamline the process behind range planning, stocking, and to improve the overall understanding of the products on the retail floor. With the app, users can scan a QR code associated with any product to quickly gain access to the data and analytics filtered to that specific product. This allows the user to be more informed when it comes to decision-making. Additionally, users can view a 3D model of each product within the HoloLens environment, allowing the user to visualize the layout of the aisles in real-time.

HoloLens gives retailers easy access to data reports and models of their products while on the store floor, enhancing the relationship between the store managers, employees, and the products. Employees can view models to help find products they aren’t familiar with and real-time data reports help managers make decisions on product placement and orientation all while being able to see the physical layout with holograms projected from the HoloLens 2.

Want to learn more about Infocepts?

Infocepts is a data solutions firm that uses services to drive business outcomes with data and analytics. Through a solution-oriented approach, we guide the modernization efforts of our customers, enabling you to make truly ‘data-driven’ decisions so your users can make smarter decisions and businesses can achieve better outcomes.

To find out how Infocepts can help you during your business intelligence migration and beyond, connect with one of our experts directly.

Find out more about Microsoft HoloLens 2:

HoloLens 2 offers the most comfortable and immersive mixed reality experience available enhanced by the reliability, security, and scalability of Azure.

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The banking and financial services industry is challenged to stay profitable despite frequently changing economic, regulatory, and technological changes across the globe, using big data and analytics to understand consumer behavior, train predictive models, counter fraud, and find new business possibilities every day.

The Basics of Banking

Banking is the cornerstone of any economy’s financial well-being. Banks allow consumers and businesses to make secure financial transactions and investments, obtain lines of credit, and save and grow their money. In the 20 years, technology has advanced traditional banking from strictly brick and mortar branches, and into the hands of consumers through online banking and mobile apps. However, the basics of this ancient system remain intact: the need for standardization of procedures that govern loans, set interest rates, and guarantees behind currencies.

The banking industry, as it currently exists can be divided into two categories: commercial banking and investment banking. Commercial banking represents consumer and business banking including commercial banks, foreign banks, savings and loan associations, credit unions, thrifts, and other savings banks. Investment banking and brokerages include managing portfolios of financial assets, trading in securities, fixed income, commodity and currency, corporate advisory services for mergers and acquisitions, corporate finance, and debt and equity underwriting.

Banking Industry Trends

The banking industry is ever attempting to catch up with advancements in its consumers’ preferred way of doing business. Technology, fintechs, and non-banks are all challenging traditional banking institutions to keep up with consumer demand in new ways. However, given the risks involved with potential data breaches and loss of funds, the financial services sector has been slow to invest in this strategic change.

Ultimately though, consumer demand is forcing online banking, mobile phone applications, banking services from non-banking companies, cashless purchases, and increasing consumer debt levels. However, the fundamental challenge of the banking industry remains the same, how to provide services while making a profit. There are several strategies that banks can employ to deal with these challenges.

Analytics is the answer

When it comes down to it, each of the 4 strategies, encompass analytics at its core. Reason being that financial services cater to millions of customers and when you market to large segments, using analytics you can see tangible impacts and high ROI solutions that can be delivered. Breaking it down further, customer analytics for the financial industry, can be categorized into 6 broad categories:

It is said that it is more profitable to retain a customer than it is to acquire a new customer in the present day. Banks are constantly at risk of losing customers and to control this, they may offer their best customers better rates, waive annual fees, or prioritize treatments. However, such retention strategies have associated costs, and financial institutions cannot afford to make these offers to every customer. The success and practicality of these customer retention strategies are dependent on identifying the right action for the right customer. Using customer analytics in the above 6 broad categories, banks are able to better understand their customers and ultimately better serve the market and overcome their challenges.

Case Study: Global Financial Institution

A global financial institution’s client coverage group needed an efficient, automated system to use to view, onboard, and manage their clients and all related data. We developed an award-winning one-stop portal for the customers that enables managers to monitor key performance indicators, instead of relying on Excel-based reporting from multiple sources.

How We Helped:

We worked closely with our customer to understand the company’s core needs and to develop a relationship manager portal that improves the efficiency and effectiveness of thousands of commercial banking relationship managers and the management team.

The portal provides integrated functionality in three distinct areas:

  • Social: blending all social channels information in one place, allowing RMs to discuss client opportunities and share ideas.
  • Client Reporting: Featuring customized performance reporting and client-specific reporting on KPIs
  • Tasks: RMs can submit, manage, and track service requests, allowing for better management of existing and new clients

Read more on our Client Reporting and Performance Management Portal for Bank Relationship Managers.

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Data collection for retail shops has grown from the manual inventory of receipts to sophisticated POS systems nowadays. However, retailers everywhere are facing new business disruptions on the account of the global COVID-19 pandemic. They are racing to adopt new strategies to be successful moving forward. What store locations should open first? What are the shopping patterns and can we improve them for social distancing? Where is my supply coming from and what should be restocked? As such, real-time data and analytics will have a key role to play when operations resume.

Decisions made in a silo will unintentionally affect other aspects of the business. Making sure you not only have the data and insights but really understanding what is happening during the full lifecycle of your business is imperative. Knowing your customers so you can provide recommendations, partnering with suppliers to improve product availability, assisting ground staff to optimize store utilization and services, benchmarking competition, and pricing to stay ahead will all need timely data and powerful insights delivered in near-real-time.

Making Data-driven Recommendations

  1. First, establish robust ground communicationCommunication will be vital, with both your ground staff and customers. Create a centralized ‘response team’ to maintain real-time contact with stores to understand and solve challenges being faced on the ground. Extend relevant information to customers, to help them make more informed choices. In addition, share updates on specific efforts taken to keep customers and workers safe, for example, special store hours for seniors, items tried or returned to be held for 24 hours and more.
  2. Leverage digital transformation in retail – Focus on technologies that can help in these times, such as:
    • Store navigation, live digital interactions, geo-fencing, and wayfinding solutions – ensure customers can find the products they want as quickly as possible.
    • Scan & Go using a mobile app – allows customers to reduce human contact and avoid standing in line.
    • Click and collect – order products online (mostly via a mobile app), and collect them by the entrance of the stores, from special “parcel locker” nearby, or other supporting locations.

    A good example here is from one of our key customers. they recently released a series of live stream sessions and short videos to introduce its new range of customizable handbags. These broadcasts were bolstered by other digital experiences, including a mobile lifestyle quiz leading to personalized handbag recommendations from the brand’s namesake designer.

  3. Push the pedal on omni-channel strategyAn omni-channel strategy is a must to ensure movement in the stock levels. Managing inventory centrally and servicing ecommerce orders through in-store inventory will be essential. Incentivize in-store customers to adopt technology to experience and buy products online or remote shop at the store. Shift workforce to support order picking for ship from store, click & collect, home, and curbside deliveries.For example, one of our customers, PetSmart’s launched a hugely successful curbside pick-up program where customers can order online, drive-up, check-in with mobile, and pick up the merchandise safely. PetSmart revamped its mobile platform and re-assigned its store associates to support curbside deliveries and was able to deliver essential products and services, even in high-risk areas.
  4. Enable access to analytics that helpsDeploy artificial intelligence (AI) or machine learning (ML) based analytics using hot data sets to aggregate and provide real-time intelligence on changing consumer behavior and happenings in the stores around social distancing, traffic management, and occupancy rules. Provide real-time intelligence on product demand, product availability, competitor, and price benchmarking quickly to make swift decisions. Implement ‘triggered alerts’ as a way to notify customers in real-time about the products they most care about. Automate price drop, low inventory, and back in stock messages to those who interacted with specific items. This can become a major differentiator and greatly enhance customer experience.

Recognize Their Opportunity to Make Data-Driven Decisions

This time can be intimidating, but using data brings clarity and instills confidence to make appropriate decisions. This is the perfect time for retail companies to use data to do what is best for the business and workforce. The role of data and analytics is rapidly changing from simply acting as a business-supporting function to a catalyst for digital transformation.

Hear Infocepts CEO Shashank Garg’s point of view as he engages in a dialogue with Evan Levy, a recognized thought leader in data and analytics. They discuss how businesses should invest in their D&A programs today to achieve a sustainable advantage for their business and mission. Watch now.

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According to various estimates, internet users today attract an overwhelming number of advertisements (ads) as per their browsing behavior. Advertisements disrupt the users’ browsing experience by appearing on desktop and mobile devices in the form of pop-ups, banners, auto-play videos and full-screen images. Ad blocking tools have sprung up as a natural response for neutralizing them. Over 200 million users of such tools bear testimony to the fact that internet users dislike online ads.


Ad blockers are giving advertisers a run for their money by ensuring that ads never reach their intended audience. As a result, they have dented the revenues of online business entities that rely on them to sustain themselves. A 2016 estimate pegs global losses due to ad blockers at a whopping $40 billion.


Are Advertisers and Publishers Effectively Dealing with Ad Blockers?

Several websites block access to their content unless users deactivate ad blockers or pay to access content. It is an attempt to counter the revenue crunching effects of ad blockers.


Research shows that such measures are counter-productive. A huge chunk of internet users would rather stop visiting such sites than being arm-twisted into paying up or disabling these tools. Nonetheless, the majority of users are not averse to watching ads as long as they are not annoying. It sets a clear mandate for advertisers and publishers to serve ads that enrich users’ browsing experience.


Is There a Way to Enrich Users’ Viewing Experience?

Yes, advertisers and publishers can start by identifying non-performing ads. An ideal way of identifying non-performing ads is to map the browsing behavior of users with multiple ads. However, it is impossible to accomplish this task manually when you take browsing behavior of millions of users into account.


Our client who is a media conglomerate was facing a similar problem. One of their biggest advertisers wanted their ads to appear only in certain premium slots. Our client carried out a manual inspection and discovered that the advertiser’s ads appeared randomly in viewer streams. They were aware of the problem and had huge volumes of data at their disposal to affirm the veracity of the issue. In spite of this, they were unable to fix it. The reason behind this was the huge time and effort required for extracting insights from data rendered it un-actionable. They were in need of an automated solution to achieve efficiency and address the issue.


How Our Solution helps in Enhancing Viewing Experience

We wanted to make efficient use of the huge ad server data our client had at their disposal. We, therefore, built an automated BI mechanism that analyzes and reports ad server data in a meaningful way. Our solution addresses three major concerns –


1.      Repeated ads

When users see the same ad multiple times in quick succession, it leads to Ad Fatigue. Repeated ads become less effective as viewers learn to ignore them. Our solution helps in this regard by identifying ads that do not adhere to frequency limits set by ad publishers. Non-adherence with pre-set frequency limits indicates an issue with the ad server. Also, our solution helps account executives to serve advertisers better by highlighting ads pushed in the ad server without a frequency limit.


2.      Repeat ads of same industry category

Competitive ad separation is an essential requirement for online ad publishers. It dictates that two ads belonging to the same industry category should not appear during the same ad break. For instance, a McDonald’s ad should not immediately follow a KFC or Burger King ad during a viewer’s scheduled ad break. Our solution helps in avoiding such situations and helps publishers in drafting better ad media plans.


3.      Ad Load Latency

Ad Load Latency is the time it takes to load an ad at the start of an ad break or after a previous ad ends. While the latency is too small for most users to notice, it may become noticeable while rendering ads of heavier file size on networks with slow bandwidths. This issue has a significant impact on users’ viewing experience, and publishers strive to keep the latency as low as possible. Our solution tracks the latency of every ad with details about the resolution at which the rendering took place. It allows ad publishers to address the issue holistically.


The Impact of Our Solution

The following numbers give a statistical account of the impact made by our solution –


  • 99% Reduction in offending ad views: An offending ad view occurs when the same user views a particular ad multiple times in the same viewing session on a particular site under analysis. Offending views dropped by 99% as compared to earlier, e., before implementation of a fix to the ad server based on our solution
  • 85% improvement in problematic viewing sessions: A viewing session is a unique viewer’s single continuous viewing session. A problematic viewing session is one which has at least one offending Ad view as per the definition above
  • Saved millions of dollars: A fix to the ad server based on suggestions from this solution’s implementation has saved millions of dollars in lost ad revenues on a yearly basis

Our solution has helped in demonstrating to our customer that effortless exploratory analysis of their ad server data is possible. It has helped them in discovering ad view patterns and identifying areas of revenue leakage. Apart from saving millions of dollars for our client and enhancing their users’ viewing experience, our solution has armed them with insights to create a formidable media brand.


As our client’s understanding of the possibilities unearthed by the solution continues to grow, so does our capability to guide them through the advanced stages of business intelligence.

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As more consumers turn to on-demand video streaming services such as Netflix and Amazon, media entertainment companies are moving from the traditional DVD/Blu-ray market and further into online streaming.  But, with an almost endless amount of content choices available to consumers, media companies are looking for new ways to attract consumers’ attention to their unique content.


To help uplift and promote its home entertainment digital business, one multinational media company launched a new content initiative—developing bonus content like deleted scenes and wallpapers for the titles they have available for viewing on household devices and set top boxes.


Since this was a new initiative for the marketing and branding team, they had no system in place to track how the bonus content was performing and whether it was effective in increasing viewership of their content.


The Difficulty of Effectively Tracking Bonus Content

At the start of its bonus content initiative, the only data which the media company received from various streaming service providers was basic point of sale (POS) information that showed what movies or series was sold or rented and the number of sales. While this information helped the company determine what movies or series content was doing well, it didn’t allow them to see if the bonus content was helping boost their sales or not. And, even more specifically, what type of bonus content was most effective.


At the time, the company was using MicroStrategy as their reporting platform. However, they had limited knowledge of its capabilities and were only using its traditional form of grid style reports downloaded to Excel to determine high level behaviors of movie content. This functionality was not enough to allow them to track the user experience with bonus content. So for help, the media company turned to Infocepts.


Using Meaningful Data and Visualization to Inform Decision-Making

Digging into the project, the Infocepts team discovered that the web logs of the content servers could be used to identify the user behavior. This data could then be sourced to a pixel perfect dashboard on a daily basis to build pre-canned and meaningful visualization.


The Infocepts team thus built a user-friendly pre-canned dashboard that integrates the data from web server logs and makes it easy to understand the user analytics and their experience with bonus content. The dashboard includes at-a-glance, visual metrics on a number of criteria, such as:

  • Overall traffic analysis summary across bonus content
  • Title performance analysis from historic period thru current date
  • Popularity by content type
  • Title performance comparison of current vs. previous month
  • Total number of active days of the bonus content across a title or movie

This dashboard not only provided the media company with traffic analysis over hosted bonus content, but through visualization also helped the branding team to review their strategies and plan and release their budget for hosted bonus content based on the popularity and viewing experience of the users. By looking at the historic trend analytics on the dashboard, the team can determine what type of bonus content will perform best.


For example, the marketing and branding team discovered that videos and gallery content (wallpapers and screen savers) were the most popular types of content, while interactive was the least popular. Since interactive content is quite costly to develop, having these insights helped the team use their resources wisely and focus on the types of content that perform best for the titles that are most popular.


Additionally, the dashboard helped the content team track and direct their resources to popular titles rather than developing new bonus content for less popular or non-performing titles or movies, which is based on the historic trend of viewing experience of the bonus content.


Post Live Impacts of the Dashboard Analytics

The dashboard Infocepts created for the marketing and branding team has been a powerful tool to help the team achieve its goals. Specifically, once the dashboard was implemented, the team was able to use analytics derived from it to:


  • Substantiate ROI: The analytics dashboard allowed performance KPIs, such as hits/views and unique users, to be readily available to decision makers so they could measure marketing performance and justify the ROI of bonus content.
  • React and answer in real time: Thanks to dashboard analytics, the branding team could respond to real-time data insights. For example, the team tracked performance KPIs for the new bonus content to understand how the content was performing during its initial stages of go live and was then able to make adjustments to the content based on the real-time data insights to boost hits and engagement with users.
  • Fact-finding analysis: The dashboard also proved its value to perform investigative analysis. For example, spikes on certain days of the month or the far better performance of hits seen during summer vacation than over the Thanksgiving holiday helped the marketing team to effectively plan campaigns for the calendar year around these trends to boost sales.


More Insights to Come

The information the marketing and branding team is getting through the dashboard is already helping to drive more sales through better promotion and production of bonus content. However, there is still more personalized insights that additional data points could offer.


In the next phase of the project, the Infocepts team will incorporate geolocation and time log data into the dashboard. This information will help the marketing and branding team create promotions and content for specific geographic regions or times of day.


For example, specific titles may do better in certain geographic regions than others or different times throughout the day may have higher viewership opportunities. With this information, the marketing and branding team can further personalize its promotions and content offered to sync better with geographic or time of day preferences.


Ultimately, thanks to the insights that the team has been able to extract, the marketing and branding team has been more successful in creating and promoting bonus content that achieves the team’s goals—engaging end users in a meaningful way that pushes them to stream their content over other choices available.


Does your marketing team have a complex data analytics problem it’s trying to solve? If so, let’s talk. We can help.

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