Digital technologies have been completely democratized over the past several years, which is producing mountains of data related to customer behavior, from preferences to interest and sentiments.
Because of the COVID-19 pandemic, customers aren’t using the same channels they’ve traditionally used to make purchases, which has accelerated the need for business to more efficiently gain actionable intelligence from the info they’re collecting.
Businesses want to apply technologies like artificial intelligence, machine learning and natural language processing to better understand customer patterns and make predictions that will enable a more personalized experience, but poorly organized, unstructured data is holding them back.
Deploying digital systems of engagement that need to deliver a personalized experience – online store, chatbot, mobile app – without effective data analytics will lead to poor digital experiences.
Marketers and other business users that face challenges with using data analytics effectively need to ask three questions. 1. How do I accelerate? 2. How do I automate? 3. How do I reduce my cost per insight?
Here are four key best practices to keep in mind as businesses look to become more data-driven:
Seven to 10 years ago, before digital technologies became so prolific, it could take several years of interactions and purchase history before a business could completely understand that customer’s buying behavior.
Today, analyzing a minute of history on a customer’s buying behavior could change your understanding of their buying pattern. Companies need to develop and deploy data analytics and intelligence systems of record at lightning speed. This will allow your business to reduce the time to insight, while also optimizing cost per insight.
Today, no one can claim that technology is a problem when it comes to visualizing and interpreting business info.
There is a continuing proliferation of technologies like Hadoop, MongoDB, Spark, Snowflake, visualization tools like Tableau, Looker, Microsoft PowerBI, TensorFlow, machine learning algorithms and more sophisticated cloud data analytics.
Technology, systems and computing power are available at scale. What’s holding back companies from using many of these technologies effectively is partly their investments in legacy systems, and partly having information in silos where it’s not required and lack of strategy to modernize.
Organizations need contextual information that is centralized for distribution and analytics consumption.
Many marketing organizations and other business users are investing in data lakes and centralized data warehouses to store info from multiple, diverse sources. Even though these are business-sponsored, they are still IT-centric.
With IT centric approaches, there are bound to be silos. For a retailer, this means brick-and-mortar stores aren’t communicating with omnichannel and the supply chain isn’t communicating with inventory management – and every possible combination in-between – creating a lag in the consumption of that info.
This is where data mesh architectures hold promise – to distribute data at scale in a way that centralized platforms can’t, and also give the business insights and automate decision-making.
Data mesh gives business groups the flexibility to view info and make decisions. Data mesh is an approach that will enable organizations to make use of many diverse sources of data, breaking the silos that sometimes confront info lakes.
Years ago, the CIO made most of the decisions around data analytics, customer success and business analytics initiatives. Today, the entire C-suite and key stakeholders within the business are deeply engaged which often leads to friction and silos.
The IT department still has an important role to play in standardizing the tools, technology and infrastructure. But as the consumption patterns and requirements around data differ, the marketing organization and other business users need to collaborate with IT to understand how they can work together more effectively to leverage their info.
Marketing organizations have come so far in gaining insights from info, especially in the realm of customer success. But, the questions of how to access it, how to automate it, and how to optimize cost per insight, still needs to be answered in order to be successful moving forward.
The challenge is by no means trivial. But the potential rewards, in the form of data-driven experiences that delight customers, more efficiency and automation, are exciting to think about.
Radhakrishnan Rajagopalan is the global head of customer success at Mindtree, a leading digital transformation and technology services company.Reblogged 1 month ago from www.clickz.com