Click to learn more about author Chirag Shivalker.

Customers are engaging with businesses through ever increasing touchpoints including websites, social media, in-store, mobile, and tablets. It’s really ironic that irrespective of how they engage with your company, they expect customized, personalized and consistent experience every time. These expectations have proved lethal for enterprises to fulfill, as a lot of them are not able to manage the humongous amount of data and neither are they able to analyze that data to glean insights as to how to effectively engage with every single individual.

In this data-driven age, brands must not struggle, instead, are required to fully leverage sources of data for insights. However, as mentioned in the article ahead, with so much of data types it is quite challenging for them to tell the difference between various data types.

Structured / Semi-structured / Unstructured

Few of the organizations that you come across somehow manage to extract information from structured data which is usually in form of a fixed field record or files. But they struggle immensely to collect and transform unstructured data from, which is anything that is not in traditional row-column database setup.

Unstructured data is an assortment of both text and multimedia content. Approximately 80% of organizational data is unstructured and with data influx witnessed in the last decade, it has grown twice the size of structured data. This is not only created data collection and management challenges but also made data processing a mandate for enterprises. Unless the data is processed it could not be analyzed and without analytics of data, it is impossible for organizations to glean insights to make profitable growth.

Collect, Process & Load – CPL

This refers to the activity, also known as data processing, which facilitates database usage and data warehousing. It is an assortment of data collection, data entry, data categorization, and validation. The staggering growth in the amount of unstructured data has made the process of data transformation far more complex to be handled by in-house teams of most of the enterprises.

Considering the kind of growth and importance of unstructured data and the impact it has on organizational decision making, CPL solution providers offer customized data management services of transforming your data so that it can be conveniently integrated with structured data for further usage.

There are data processing experts equipped with latest technology and tools for data processing and analytics, assisting diversified domains of construction, real estate, banking and finance, healthcare, manufacturing, education, travel and hospitality, transportation and logistics, eCommerce and retail, and many more to move ahead of the tedious task of managing unstructured data.

NLP – Natural Language Processing for Social Listening

Enterprises these days are busy chasing the opportunity to know what their consumers, existing and potential, think and feel about their services and products, by collecting data to gather insights that can be used for market and business intelligence. Currently, organizations are busy reaping benefits of both linguistic or grammatical, and machine techniques. Converting unstructured data, such as text and multimedia, to structured data helps companies with insights solutions including social segmentation which further helps them to design, develop and execute targeted marketing campaigns.

Experts at sentiment analysts, aka opinion mining, successfully stream, interpret, and bring together opinions, moods, and feelings across the web. The advanced data analytics techniques they use empower enterprises with flame detection (bad rant), new product perception, brand perception, reputation management etc. Their NLP solutions generate insights from varied data sources, including:

  • Browsing behavior
  • Browsing devices
  • Census information
  • Color preferences
  • IP address
  • Language learning preference
  • Multivariate testing
  • POS activity
  • Previous campaign activities
  • Purchase history
  • Similarity clustering
  • Social activity
  • Social influencers
  • Survey responses

Companies are struggling to survive in this data-driven , and facing challenges of collecting data and analyzing it on a real-time basis. They need to do this so as to make that data instantly actionable, in a predictive way. If a company does not have even any one of these capabilities, their marketing messages would be less compelling and will observe deteriorating response rates. On the other hand, brands that have embraced real-time contextualization with help of best data processing experts see and vouch for huge uplifts in campaign responses.

Marketers now have known the imperative of these multichannel, contextualized communications with their customers. They have realized, more personalized the experience, happier is the customer. Customer who wishes to purchase more is not a happy customer. Instead, it’s a customer that is retained, upsold and more importantly the one who advocates your brand.

Final word

Companies should get rid of guesswork, and try and make sense of web pages and marketing program results. Reach out to the Business Intelligence with help of Data Management experts, who guide you outright about which marketing channels to focus on. They save you time and money by doing more of what works more and less of what does not. Furthermore, they help you create loyalty.

Bringing in first-time buyers is one thing, and convincing them to buy again and again is different, and these data gurus know the way out. They help you crush customer churn by telling you what your customers want before the customers even know they do. Their insights will help you understand customer behavior past and predicted, enabling you to find more of your best customers and help re-engage lapsed ones. Their visually rich dashboards and custom analysis/reporting give your company everything required to turn data into revenue and customer loyalty – quicker and more effectively than ever before.



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Bigdata and data center

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