There’s little doubt that the currency of the modern world – the so-called “fifth fuel” – is data. Data is being captured everywhere, at all times, so that the volumes of global data are constantly multiplying. The question is, how do you make sense of all that data?
“Big data” – made powerful by huge advancements in AI and machine learning – is now making instant decision-making a reality.
There are three areas where Big Data is transforming the consumer goods sector:
Let’s examine each, with real-life examples.
Marketing is one of the most obvious areas where Big Data has had a measurable, positive impact. There are multiple ways that marketing in the consumer goods sector can be digitally transformed. Using data, we can build a 360-degree view of a customer that includes their shopping behaviour and the effectiveness of marketing efforts to cause them to buy. We can then calculate the ROI of marketing efforts in the domain of a particular customer, building a picture of their ‘performance,’ or responsiveness to marketing efforts.
At the Data Innovation Forum for Salesforce Architects, Jitendra Zaa, Associate Partner and Salesforce Technical Leader at IBM, said: “Retailers have so many methods of pushing products to customers. Website, email, social media and so on. Reams of data are created. Our data sets can answer questions such as: Which product is selling? What is the ripple effect for different products? For example, maybe marketing is working for product A, and that causes product B to sell better. Analytics is our best shot at making sense of these trends.”
Where do CRM tools come in? At Odaseva we provide the backup and restore element of the Salesforce ecosystem. Salesforce – the market leader – is excellent at tracking operational data. The next step in digital transformation is to turn or link operational data into transitional data so it can be effectively tapped-into for instant decision making.
To that end, backups don’t have to spend their lives sitting idle in a repository, waiting for the day they’re called upon to recover data. In an excellent case of “killing two birds with one stone,” it’s now possible for IT to make intelligent use of data backups. An extraction tool can data out of backups of the CRM platform into a ‘data lake’ where it can then be used in an analytics framework. Data from backups can power analytics, ML and AI processes to drive business decisions.
In the early days of Big Data, the supply chain and sales operations worked in discrete silos. Retailers were not equipped to connect the dots on how marketing was directly (or indirectly) affecting sales.
That’s a problem, because the ability to forecast data is a critical capability for growing sales. Marketers need to be able to answer questions like:
To address questions like these, organisations need a system in place that can rapidly analyse terabytes of data to forecast demand in the coming weeks or months. These analyses can inform merchandising operations to ensure the right products are available at the right time to meet customers’ needs.
Jitendra highlighted a concrete example during the Forum: “Stockers are tasked with keeping shelves full of the right products. Stockers need to know: Which product will be popular in the next week? In the case of a grocery store, should they display healthy foods or snack foods? Using Big Data, stockers can take a picture of the shelf, and a mobile app will show them the right answers using a powerful AI based model & API. For example, shelve more sweets and chips in the weeks leading to Christmas. That’s where AI is coming into picture. It’s informing the forecast for each product.”
The above example considers a physical store with physical shelves, because Big Data isn’t limited to online commerce. Big Data has enormous applications for traditional brick-and-mortar stores, as well.
Jitendra explains, “In retail we have a habit of thinking that bricks and mortar is going away, and everything is going online. However, the one hurdle is the time that it takes for consumers to get their deliveries. Less than 1% of the world population is getting same-day delivery of items. For the foreseeable future, bricks and mortar isn’t going away. Online is penetrating, but we cannot completely lose focus on physical stores.”
How do large enterprises, like national grocery store chains, take advantage of these data processes for product forecasting? There’s no single silver bullet. Most multi-national organisations are working with a hybrid cloud solution. It’s not uncommon to see 10 different tools for end-to-end business processes. ERP, CRM, supply chain, data warehousing – they all have to fit together like Lego blocks to work in harmony.
Odaseva’s tools provide one component in this landscape, enabling organisations to extract data from Salesforce, get it into a data warehouse, and feed it to an AI system to conduct data modelling on this data. That is a powerful capability which adds a new Big Data aspect onto systems that are already in place without having to alter the parts that are already working well. In this way, we can drive this intelligent sales forecasting without starting from scratch.
While sales is about forecasting consumer demand, supply chain is about forecasting the movement and prices of goods. Following the explosion in demand as pandemic public health measures have loosened and the subsequent global supply chain crisis, accurate forecasting has never been more important. Using Big Data, we can apply proactive analysis to our supply chain in an unpredictable business landscape.
For example: Let’s say I’m a global device manufacturer. I have limited space in my warehouse. Which of my products will move and sell fast? The most important structure I should have in place to answer that question is an intelligent workflow.
Outside factors will affect the manufacturer’s capability to produce goods, such as the price of raw materials. Over the last 12 months, prices have moved in unpredictable ways, spiking extremely high and dropping very low. If an intelligent workflow that can tell us to buy raw material in the next six months because the price might go up, we are way ahead of the game and, hopefully, a step ahead of our competitors. Big Data can help us predict what’s seemingly unpredictable in both demand and supply.
At the end of the Forum, Jitendra highlighted an anonymous customer example: “For one customer, we implemented Salesforce alongside an intelligent engine. We asked the system: ‘How much time would it take a frontline employee to stock something in a store?’ The solution showed that each frontline employee would save three hours per week, after the intelligent workflow was implemented. Doing the math, that means that each employee could save 156 hours annually. With 25,000 employees accessing the system, we were able to save the customer 3.9 million man-hours. Even using minimum wage of $20/hour in our model –- that equates to $78 million in savings per year.”
The above workflow is based upon a suite of applications of which Salesforce and Odaseva are two components of many.
If you’d like to find out more, request a demo today.