91���� is on a journey towards Sustainability 2.0, an agenda that reinvents sustainability under the compelling challenges of climate change and social inequity. This new agenda is driven by a remarkable combination of thought and action on 91����’s part with meaningful public-private-people partnerships. The Sustainability Report 2.0 is available .

The Need for a Sustainability Data Analytics Platform

Research by states that 86% of business leaders have invested in sustainable practices to protect their organizations from disruptions. However, reporting on sustainability initiatives and finding various data types is difficult for all relevant parties to access. In order to drive sustainability performance management, the data analytics platform is vital.

Why is Sustainability Data Analytics Platform Necessary?

• Unable to detect human errors during data collection – Leveraging AI-driven document processing methods
• Unable to trace and audit data – Ensuring end-to-end traceability by customizing system logs as user-friendly. Approvers can easily interpret and make approval decisions with sustainability data reviews
• Time-consuming while aggregating data – Introducing the power of data aggregating capabilities by customizing industry-standard data management tools
• Inconvenience while accessing sustainability reports – Enabling self-serving capability with ideal role-based access control for different stakeholders
• Difficulty in getting the approval of reported sustainability data – Leveraging integrated workflow for end-to-end automation of processes ranging from data preparation to approval
• Proactive methods needed for meeting sustainability targets – Introducing machine learning models with the help of predictive analytics tools for preventive actions to meet the defined sustainability goals
• No single source of truth for sustainability reports – Introducing a self-service platform for accessing sustainability artifacts

How Will the Sustainability Data Analytics Platform Help?

• Integrated platform with low environment code to address the above-mentioned pain points
• Single source of truth for stakeholders for complete transparency of sustainability reports
• Secured access to data across stakeholders, data providers, reviewers, sustainability officers, and external auditors
• Automated workflow for end-to-end data management and reporting
• Complete traceability of data preparation, reviews, corrections, and approvals
• Complex calculations to arrive the critical sustainability measures across environmental footprints – energy, emission, water, and waste
• Prebuilt data model to reduce the time of implementation
• Various connectors to extract data from scanned documents, PDFs, enterprise resource planning, and excel sheets

How is the 91���� Sustainability Data Analytics Platform different from other platforms?

Here are the key differentiators that make 91���� a strong player among others:

• Single Version of Truth
• Pre-built data model suitable for manufacturing and consumer product companies
• Automation of data consistency, accuracy, and completeness verification in the data collection process
• Inbuilt predictive insights for corrective actions to meet sustainability targets
• Role-Based Access to Data
• Accessible roll-out features across businesses
• Configuration options for third-party audits
• Full Stack Solution ensures data collection till sustainability reporting and analytics
• Easy and convenient to deploy across different business units and facilities
• The functional view of each of the components is depicted below:

The 91���� Sustainability Data Analytics Platform is based on cloud technology, favouring the resource-efficient use of IT resources, and enabling every company’s flexible and expandable growth. With integrated solutions, companies can introduce sustainability goals more confidently and cleanly into their daily activities and move strategically toward building more resilient, sustainable businesses.

The post Sustainability Data Analytics Platform for Implementing Sustainability 2.0 appeared first on 91����.

It is a common situation that most shoppers face while buying packaged goods online. Due to high demand many daily used products go out of stock in retail sites. But it is more annoying to experience longer time in any shopping website due to lack of clarity about how to select and pay for a product. Direct-to-customer (D2C) is the new trend shaping the consumer-packaged goods (CPG) industry across the globe. Primary reason being low entry barrier, which is enabling private label brands to appear in the market with frequency higher than ever. However, not all brands could sustain the race of attracting consumers to their brands. It is more due to the marketing strategy they follow than the quality or features in the products. Drawing consumers to the products is the primary major challenge in this business model. Second major challenge is ensuring continuous availability of products, which means ensuring an uninterrupted supply chain process. If we introspect more into the primary challenge, we find three reasons underlying. First one is all about understanding the need of each & every customer and designing product as per their requirements. Second is keeping a tab of competitors’ offerings in the marketplace. Third is using information related to earlier two cases to develop personalised buying experience for every customer. The only solution that could help us resolve all these problems is data. Data is the ammunition in this expanding war of customer acquisition and retention. Collection of data about customers’ preferences and competitors’ new offers, storing those data and using those effectively are of primary importance in building a successful D2C business model.

If we go deeper into this, we find that next level of challenge is identification of data. It is customary for every marketer to understand and find the type of data one needs to understand customers’ requirements. Parallelly, one needs to find the data required on competitors’ offerings to strengthen the grip on the marketplace. Once understood, firms should think about the resources through which all these data can be collected and stored. And finally, how these data can be used to generate insights about customer’s behaviour.

To help firms in this journey, digital technology services are on the rise. The advent of intelligent automation has equipped the software service providers develop digital tools to understand the ongoing trend and find which areas need more focus to make the business run profitably. Robotic process automation (RPA), cloud technology, Artificial Intelligence (AI) based algorithms and machine learning (ML) programs are examples of digital tools which have paved the way for technology focused CPG firms to monitor their businesses closely and engage consumers more effectively. Every consumer has a different and unique way of shopping. Therefore, engaging with each consumer through the right channel, right promotion and right offer is undoubtedly a critical task. In top of that increasing penetration of mobile technology has brought all category of consumers at the same place and at the same time resulting in an extra level of complexity in data collection. Therefore, developing a robust data storage facility is no more a choice but a necessity for managing such increasing diversified consumer base. 91���� through its rich experience of working with multiple CPG consumers can provide both on-premises and cloud-based database solutions to manage terabytes of data on a continuous basis.

Once the data storage facility is set up, suitable data collection resources should be developed to ensure uninterrupted data feeding to the repository. Whenever a consumer engages in any purchasing activity through online channel, millions of data are transferred to the marketing firm. Multiplying this with number of available channels, nearly trillions of data are needed to be handled every day. Hence, the solution is to have resources having the capability to find relevant data in any format from any channel and incessant feeding of these data to the storage facility. 91����’s intelligent automation team can develop any customised intelligent bots using robotic process automation (RPA) for collecting data from any channel at any time with 100% efficiency. Not only from websites, but these bots can also even identify the necessary data from any document in any format. This can help the firms analyse multiple purchasing invoices to figure out average purchase value for any customer.

After collection the data, next important steps are identification of relevant KPIs and development of analytical models for the purpose of understanding the status of ongoing business and generation of insights about customer’s buying pattern. 91���� has a dedicated analytics team who has developed analytical models as per business need and has helped firms understand consumer journey at every touchpoint starting from exploration to procurement of any product.

After data management the next critical area where D2C firms should put highest focus is managing the supply chain of entire processes. If designing products as per consumer’s need is the primary challenge that D2C firms face now-a-days, then the next big challenge is taking these products to the consumer’s doorstep. The solution that addresses this concern is smart supply chain. Although most of the firms engage third parties for delivery of products, making the products available to delivery partner’s warehouses or distribution centres need a thorough monitoring of supply chain. 91����’s Industry 4.0 and Digital team together provide efficient and effective supply chain solutions with the help of Internet of Things (IoT), RPA and AIML. Not only our solutions ensure automated monitoring of entire supply chain process but also can eliminate the redundant processes, reduce the energy consumption at intermediate stages wherever possible and build models for optimising selection of packaging materials.

The problem that Sanaya faced at the beginning of this article could have easily been avoided had the brand manufacturer focussed on these issues and collected data about Sanaya’s buying pattern. Also, they should have ensured availability of the product at retailer’s site by adopting smart supply chain practices. Emerging digital technologies like robotics, artificial intelligence, cloud technology and IoT are a blessing for firms. However, leveraging these technologies to maximise the profit and capture the market share needs a blend of expertise & experience over the years. 91����’s rich experience of working with global CPG manufacturers in multiple channels and multiple regions for diversified products have helped develop suitable digital technology-based offers for D2C firms. In an industry like CPG where competition has been a paramount concern from new product perspective, emergence of D2C practice has made the situation more complex. To compete successfully and lead in the marketplace, firms must embrace the digital tools for navigating through the wave of unlimited new products.

Author:

Debal Chakraborty,
Principal Consultant

The post Brands that Embrace Digital will Stay Ahead in the D2C Model appeared first on 91����.

The question now is what is the way forward? What is the most appropriate model for your organization?

The truth is that there is no one-size-fits-all solution. Over the last decade, Data Lakes grew to be the de facto model for modernization. These days, they are being supplanted by, or in many cases have been subsumed into, Data Meshes. Both models have their votaries, and both come with their own set of challenges.

Let us examine these two models in a little more detail so that you can wrap your mind around them more easily and be better positioned to choose between them.

The Data Lake

A Data Lake is a large reservoir into which raw data can be poured and stored until needed. Thanks to its flat architecture, it stores data in its native format, as binary large objects (blobs) or files. It takes in unstructured data, such as emails, documents etc.; binary data like images, audio, and video; semi-structured data, such as CSV, logs, and XML; and structured data from relational databases. The extract-transform-load process happens within the Data Lake itself.��

The Data Lake can, therefore, efficiently manage the high Volume, high Variety, and high Velocity of Big Data. It also significantly enhances the value of Big Data by making it available as reports, dashboards, and applications, to facilitate better visualization, advanced analytics, and machine learning. All, of course, to ultimately empower organizations with the ability to take evidence-supported business decisions with more far-reaching impact than ever before.��

Being a single, integrated, and complete system, the Data Lake facilitates faster and simpler development of applications as well, which are based on one code.

The Data Lake can reside on the cloud, on a platform such as Microsoft Azure, or as a distributed file system such as MS SQL Server with the Hadoop Distributed File System.

However, Data Lake also has its drawbacks.��

As the volume of data increases and grows more complex, the central IT function becomes overloaded with requests and cannot keep pace. Individual project teams then try to bypass it and deploy quick fixes that are poorly integrated and create problems in the future.

What is worse, organizations keep pouring data into the Lake and eventually lose track of what it contains. Much valuable information can go unnoticed because data analysts have no knowledge vis-à-vis the data’s source domain and engage in fishing expeditions.

Many organizations have seen their Data Lakes turn into data swamps because, after a point, it entails considerable technical and organizational effort to make productive use of them.

The Data Mesh

The Data Mesh evolved in response to the many challenges that the Data Lakes posed.

Unlike the Data Lake, the Data Mesh is a composite ecosystem, not a monolith. It breaks giant, monolithic enterprise data architectures into decentralized subsystems, each owned and managed by a dedicated team.��

The Data Mesh facilitates the management, connection, and smooth flow of data from producers through to consumers, whether outside or within a Data Lake. In that sense, a Data Mesh may include Data Lakes.

Data Meshes can be said to have four pillars:

Decentralized Data Ownership

Data is owned by the entity that produces it, typically functions such as HR, Finance, Marketing, etc. Therefore, more value can be derived from it. Typically, tools such as Azure Databricks are used to process the large workloads of data.

Data as Product

Users, such as data analysts, can easily source data directly from the domain owners, who will ensure that the data is of high quality. Conflicts are eliminated by using approaches like event sourcing and CQRS.

Self-serve data infrastructure as a platform��

Domain teams can create, transform, and consume data products autonomously.��

Federated governance

Mandated universal standards to enable smooth interoperability and flow of data.

The Data Mesh brings many benefits to the table

Flexibility and Choice – Since its architecture is domain driven and distributed, you have the flexibility to choose vendors and technologies that work best for you, without getting locked onto one platform.��

Greater agility, seamless collaboration, shorter project times – Since domain teams own their data, they can operate independently, making them more agile and responsive. At the same time, since the teams are cross-functional, collaboration becomes simpler and more efficient. Development accelerates and projects go live faster!��

Superior quality – Since ownership is vested with domain experts, the quality of the data is always high. Further, by mandating universal protocols and principles, the Data Mesh promotes the delivery of data in standardized formats for easier access.��

Quick service: Data producers and data users interact based on pre-determined SLAs, which enables much faster delivery of data. All data management needs such as storage, logging, identity management, and such, which slow the process down, are handled by the Data Mesh’s inbuilt capabilities.��

Scalability: Being distributed in structure the Data Mesh is also eminently scalable with minimal disruption.

So, should your company upgrade to a Data Mesh?

A Data Mesh certainly sounds like a panacea for all data ills but, like all technology solutions, it must be opted for after due thought and diligence. Keeping the following factors in mind will help you make a better-informed decision about whether your organization needs to upgrade to a data mesh.

Duplication of data: Repurposing data to serve another domain’s needs may lead to data duplication. This can lead to higher storage requirements as well as increased data management��costs.

Quality Avoidance: The availability of multiple data products and pipelines may lead to non-compliance with governance standards. Therefore, these principles will need to be clearly articulated and compliance enforced through appropriate measures at the domain level.��

Change management efforts: Deploying data mesh architecture and decentralized data operations will entail organization-wide change management efforts. You will need to plan to allow for business disruptions and to ensure that critical operations continue.

Choosing future-proof technologies: Teams will have to think long term when selecting technologies that will be standardized across the company, to ensure easier future upgradation with minimal disruption.

Cross domain analytics: Reporting becomes decentralized as well, and a separate organization wide model may need to be defined to consolidate diverse data products into one report.

Talk to us at 91����. We’ll undertake an assessment of your existing digital landscape, identify modernization areas, build a strategic roadmap, and define the enterprise architecture you need.��

Build a Scalable, Flexible & Secure Data Ecosystem | Enable Insights-powered Decisions | Be Future Ready to leverage AI/ML | Gain Business Intelligence Faster and Cheaper | Enable Rapid Value-led Experimentation | Drive Data-as-a-Service.

Click here for Part 1 of blog: Modernize the Data Ecosystem to Lay the Foundation of an Insights-driven Digital Next Enterprise (Part 1)

Reference:

Zhamak Dehghani
Data Mesh Founder

Author:

Bhagaban Khatai
Data Transformation Leader

The post Data Modernization – What is the best route for your transformation journey? (Part 2) appeared first on 91����.

That said, technology leaders need to assess the pros and cons of a modernization exercise. Businesses must study the various avenues for modernization and choose the one that gives them the best cost-benefit balance. As with any change management initiative, it is disruptive and entails focused deployment of resources.

In this article, I will discuss three frameworks/platforms that, we at 91���� have helped our clients use to effectively leverage data for business success.

The Data Warehouse

The Data Warehouse was probably the first enterprise-level platform to use data for business decision support. It came into its own in the Nineties and at the turn of the new Millennium. As its name implies it organized data in structured and labelled fields that could be easily accessed, and it worked excellently.��

Data-driven business intelligence, as a concept, gained massive leverage thanks to the Data Warehouse. However, like its counterpart in the real world, the Data Warehouse’s key drawback is poor scalability. It works on pre-built schema and can take in only structured data. As a result, the data is siloed and not all data is captured.��

As the three Vs of data – volume, variety, and velocity – grow, as in today’s age of Big Data, the Data Warehouse becomes unwieldy and inefficient. And data’s fourth V, veracity, suffers in consequence.

This is not to say that the Data Warehouse has outlived its utility. It still works efficiently for businesses that deal with a smaller volume and variety of data and provides excellent decision support intelligence at a relatively lower investment.��

The Data Lake

The Data Warehouse’s inherent problems gave rise to the Data Lake, a platform with no hierarchical structure that is more attuned to the needs of Big Data.��

A data lake is like a reservoir into which raw data can be poured and stored until needed. It has a flat architecture and takes in data in their native formats – emails, documents, images, audio, video, semi-structured data, such as CSV, logs, and XML, as well as structured data from relational databases.��

The extract-transform-load process happens within the Lake itself and data is presented as reports, dashboards, and such, to facilitate better visualization and more accurate analytics, as well as to enable machine learning.��

The Data Lake is thus capable of managing the high Volume, high Variety, and high Velocity of Big Data.��

However, the Data Lake also has its drawbacks.��

Once data is put into the Lake, it becomes monolithic. This limits the knowledge that data analysts can gain from it and increases the risk of valuable information going unnoticed.

Its centralized control structure stretches the IT team thin. Projects get delayed, forcing teams to resort to poorly integrated ‘quick-fix’ solutions that eventually compound problems.

Consequently, it often ends up as a huge unmanageable data dump yard. Drawing any useful sense out of the Data Lake becomes a complex, expensive, and resource-intensive task.��

It is in response to these problems that the concept of a Data Mesh came into being.

The Data Mesh

Unlike the Data Lake, the Data Mesh is a composite, integrated ecosystem, and not a monolith. It is composed of decentralized subsystems or domains, each managed by a dedicated team. In a sense, you can say that the Data Mesh as a whole is greater than the sum of its parts.

It thus offers several advantages over the Data Lake.

It makes domain experts owners of their data. Thus, there is no danger of valuable nuggets of information being lost or ignored.����

It treats data as a product and enables a smooth and secure flow of data from producers to users, whether outside or within a Data Lake. In that sense, a Data Mesh may include Data Lakes.��

It encourages cross-functional teams and empowers them to operate independently, with little or no support from a central IT function. Collaboration is more efficient, the pace of development accelerates, and projects go live much sooner.

Its decentralized approach gives you the flexibility to choose vendors and technologies that work best for you, without getting locked onto one platform.��

A Data Mesh can be deployed for a broad range of needs and for diverse use cases:

• Migrating applications to the cloud
• Modernizing data lakes to make data more easily accessible
• Integrating apps, IoT, and analytics in real-time
• Streaming data pipelines within or from data lakes
• Data-in-motion analytics

So which modernization solution is best for your organization?

Talk to us at 91����. We’ll undertake an assessment of your existing digital landscape, identify modernization areas, build a strategic roadmap, and define the enterprise architecture you need.��

We are part of the Data & Analytics transformation journey over last 15 years

Build a Scalable, Flexible & Secure Data Ecosystem | Enable Insights-powered Decisions | Be Future Ready to leverage AI/ML | Gain Business Intelligence Faster and Cheaper | Enable Rapid Value-led Experimentation | Drive Data-as-a-Service

Click here for Part of 2 blog: Data Modernization – What is the best route for your transformation journey? (Part 2)

Author:

Bhagaban Khatai
Data Transformation Leader

Reference:

Zhamak Dehghani
Data Mesh Founder

The post Modernize the Data Ecosystem to Lay the Foundation of an Insights-driven Digital Next Enterprise (Part 1) appeared first on 91����.

Imagine you are buying a few bags of potato chips to snack on. One bag has fewer chips than expected; the chips do not match the weight mentioned on the package. You will be disappointed. You will either complain to the manufacturer or simply stop trusting the brand. Worse, you could take to social media to express your anger, affecting the broader reputation of the brand. If there are a few extra chips in the bag, you will not mind it. But those extra chips can quickly add up to massive losses for the manufacturer. Either way, not controlling the grammage of products leads to unwelcome outcomes. This is not a new problem for the F&B industry. But new solutions are now available to contain and practically eliminate-the problem.

Variances in packaged food products is expected. The manufacturer of a 78 g bag of potato chips can perhaps tolerate a variance of +/- 2 grams. But by identifying the actual variance and tracking trends, upstream and downstream systems can be improved, costs can be lowered, compliance norms can be met, waste reduced and customers kept happy by offering a more consistent product. However, usually individual checkweigher or multi head weighers and baggers are used at the packaging stage to accept/reject products for the market—when it can be too late. To overcome this, the science of Extra Grammage (EGA) Optimization needs to be mastered.

The weight of packaged food products is a tricky affair. Something as simple as extra moisture in the chips or extra oil can increase the weight. The thickness of input materials (for example, potato slices, masala or salt deposition etc. ) or even the temperature of the cooking oil or frying time can cause higher or lower weights, or the vibratory nature of the manufacturing equipment can lead to variances.������

Often the selling price of a product cannot be changed. In such cases the manufacturer must resort to controlling the weight.

EGA optimization is a latest Industry 4.0 solution integrates all weigher “Machine Data” & “Process Parameters” and where continuously monitors the trend of Underweight & Overweight Rejections and Extra Give Away of Materials.�� Then Auto-Insights are generated basis factual data at frequent intervals, minute level & KPIs are measured EGA%, rejections% etc. and any deviations beyond acceptable limits are alerted to respective operators to fix the production parameters causing the variance immediately, minimizing the volume of rejected products at the end of the line. The solution works for both linear and vertical manufacturing and packaging processes.��

Vertical manufacturing and packaging processes need to be dealt with a little differently from linear processes (example: potato chips versus cookies). In a vertical process, the product drop happens to multiple buckets during production. The weight may vary because of product breakage or due to residual recipe material, accumulated in the assembly line, sticking to the product.

In current practice, many CPG companies have not integrated their Machine data. Right set of Analytics not used to create meaningful alerts for operators and to improve output quality. Our implementation of this solution in a large food production plant has shown significant results, that even a 1% impact is saving thousands of dollars. But this is what production plant managers will like to hear the most: ROI in our implementation was less than six months. This is a high-impact, quick-return intervention that every manufacturer must consider.

Author:

Siddaraju G,
Senior Principal Consultant,
Business Consulting Group,
91����

The post A “must-have” packing weight variation control technique appeared first on 91����.

Vision-based computing or computer vision, aka machine vision, has grown tremendously in the last few years. The key reasons for the growth lie in the availability of affordable hardware, the growth in data sets and maturity in the science of Artificial Intelligence (AI) and Machine Learning (ML). With the COVID-19 pandemic accelerating digital adoption, vision-based analytics is bound to find wide-spread applications in several industries including manufacturing. The extraordinary drive for work place safety, the need for automation to improve productivity, and access to better quality will be the key drivers for the growth in vision-based computing. One indicator of its bullish future is reflected in a that found AI in computer vision market size would reach $144.46B by 2028 from $7.04B in 2020 (a CAGR of 45.64% from 2021 to 2028). Manufacturers who do not study use cases for vision-based computing in their organizations could lose out on the promise of generating business value and creating competitive differentiation.

An area where vision-based technology can be applied with great success is quality inspection. Take the case of manufacturing cookies in the food and beverage industry. Operators are deployed on production lines to check the quality of the output. They must examine each cookie for conformance to color, texture, shape, size, nut coverage and several other factors and benchmarks. These are important in an industry where consumers assess products visually. Human bias – even human fatigue or distraction — can affect the inspection process. Options such as interval sampling of products in a lab can improve the process but they too leave the gates open to flawed products reaching the market.

The solution is in using AI and ML-based algorithms to examine each cookie. Cameras scan the live production line before packaging, accurately picking out cookies that do not meet the pass range for quality. Even when samples are examined in an off-line mode in a lab, vision-based computing removes subjective biases.

Once a cookie on a production line is identified for non-conformance, a simple drop mechanism removes the cookie from reaching the packaging process.

Vision-based computing has several applications in factory environments. Most manufacturing plants already have several CCTV cameras used to monitor activity, human safety and business security. The number of cameras on a production floor can number anywhere between 30 and 100 – a number impossible for humans to continuously scan for aberrations. This is why these cameras are usually used after the fact, to investigate incidents.

However, the camera feeds can be analyzed in real time based on EHS guidelines and even used for automated risk audits. This means the investment already made in video monitoring can go the extra mile by identifying hazards in different work areas such as slippery floors, accumulation of materials, obstacles in pathways, missing or inadvertent obstructions around firefighting equipment, violation of safety norms by employees (such as not wearing hard hats or fall arrestors), unauthorized entry of personnel in restricted areas, and the entry and exit of vehicles in plants, warehouses, collection points and so on.

Not only can vision-based computing prevent issues or generate real-time alerts, but the data can also improve processes. For example, supervisors can immediately know how many vehicles are parked in a certain area, what type of vehicles are parked and for how long, the time taken to offload material from any vehicle—each data point allowing the supervisor to take quick and accurate decisions.

Vision-based computing is here to stay. And the race to adopt it has just begun.

Author:

Siddaraju G,
Senior Principal Consultant,
Business Consulting Group,
91����

The post Enhancing product quality and plant safety with vision-based computing appeared first on 91����.

Undoubtedly, social media has become one of the primary marketing channels for businesses today. It is not just used for posting ads, but successful marketers are effectively using social media for engaging the community and building followership to establish a connection with the influencers in their niche. Smart marketers leverage social media to attract customers to their own platform.

Gone are the days of spray and pray. Today’s marketers use artificial intelligence and content strategy to attract attention and eyeballs. For example, the concept of “Content is King” has now evolved into “Content is Objective”. Content today is tailored and personalized to be consumed by a focused set of people and just not generic. Content today is centred around the end consumer vis-à-vis the organization’s solutions or services. Marketing today is completely persona-based, as the organizations focus on providing experience-based marketing.

Globally, over 3.6 billion people use social media and the number is projected to grow to 4.41 billion in 2025. Till a few years ago, most companies were putting out content; but today, they are putting out interactive and actionable information and tracking the consumer’s reactions to them. Based on these reactions, the content is being changed in real-time and re-shared. This is an example of how marketers are using sentiment analysis and putting in intelligence while they market. The ROI is of course long term.

LinkedIn now has��more than 722 million users��in over 200 countries and territories. Twitter reported 187 million monetizable daily active users (mDAUs) in the third quarter of 2020, up 29% year over year. Instagram has over��1 billion monthly active users��and 500 million of them use Instagram Stories. In��Q3 2020, Facebook reported over 2.7 billion monthly active users (MAUs).

Companies today are acting on this and are actively using social listening to capture and understand their customers’ voices. Crawlers are being used to track social chatter, and this information combined with machine-learning algorithms and AI create automated actionable insights. This results in real-time brand reputation management.
It is interesting that��96% of unhappy customers won’t tell you directly, but will tell their friends about their disappointment. Also, most will post online but not tag the organization. This is where these crawlers are useful if programmed properly.

Similarly, videos do create a major impact on consumer minds. As consumers would be glued to its visual effects and storytelling. Marketers need to keep themselves constantly updated about how to influence their customers to stay loyal to their brand. They need to know where the trends are going, as businesses heavily rely on trends. At end of the day, it is all about how they position the brand uniquely and sell in new ways and being relevant to the audience in the new era.

With ever-increasing digital noise and decreasing attention timespan, persona-based marketing of curated, personalized, and interactive bite-sized content is the only way forward. Combining the power of social analytics, digital listening and sentiment analyst is the top priority for a marketer today.

Deciding on the right platform and media to deliver this content is very important. Marketers today have a wide variety of choices when it comes to the content delivery platform. The effectiveness of a platform depends on where customers are, its functionality, and most importantly the type of analytics it provides. For example, for most B2B IT companies LinkedIn is the ideal platform; while for CPG and FMCG companies, the ideal choice is Twitter and Facebook; and for Media companies – YouTube. Choosing the right platform is vital to success – but it is not the tools that make one successful; it is the skill.

Do you have the creative intelligence to thrive in this era of digital transformation?

Author:

Arijit Ray
Head, Digital Marketing,
91����

The post Marketing in the era of Digital Transformation appeared first on 91����.