Blog | Data Integration | | 4 min read

Retail Begins to Understand Seamless Integration

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It’s one thing to read about the use of data integration in a technology journal, but another thing altogether to see it in line-of-business publications. That was the case when I read an article from Multi-Channel Merchant, which covered the business reasons for retailers to leverage data integration.

“Modern software tools allow brick and mortar businesses to transition online without introducing a new business that must be managed separately. Seamless data integration and order fulfillment bridge the gap between physical and virtual channels, allowing all orders to be processed from a singular platform that accesses inventory.”

While I’m sure this seems revolutionary to those in retail, it’s actually a concept that dates back many years and includes technology that supports the integration of retail channels. The problem is that those in retail typically did not hear about data integration, or understand its value, and they are just beginning to figure out how much of a game changer it is.

The article describes “a system architecture that allows for live transactions with a single and centralized database is the primary driver for a successful approach to seamless omnichannel fulfillment.”  This configuration allows retailers to leverage and manage inventory across all channels and locations. The results are a more simplified and productive operation, and the ability to leverage the economies of scale across each channel.

Key benefits from data integration in this vertical, as covered in this article, include:

  • The ability for the retailer to leverage inventory across all locations.
  • The ability to simplify operations.
  • The ability to leverage economies of scale.
  • Faster order fulfillment.
  • Centralized inventory control.

“This integration provides customers from every channel access to the same inventory, minimizing sellouts and preventing lost sales resulting from backorders and other fulfillment delays. Retailers that eliminate inventory access issues from the sales funnel are better able to position each channel in a way that most heavily capitalizes on its inherent advantage; customers no longer must worry about which channel is most likely to have a product in stock when they all draw from the same inventory.”

The objective with data integration is to create a single logical entity from many entities that may be outside of the direct control of the retailer (such as a channel), or integrating entities within a retail company (such as inventory, sales, fulfillment, etc.). The objective in a channel scenario is to present a single set of items that are for sale and can be directly fulfilled, no matter where the items, or the data about the items, exist. An example would be to tie Costco’s online inventory to its in-store inventory.

The best examples of well integrated channels are the many innovative retail Web sites out there that are only front-ends for hundreds, sometimes thousands of companies that sell goods. Amazon.com is the most obvious example. As the Web site user, you don’t have a clue, nor do you care, who is actually storing and shipping your product. That is, as long as it shows up on your doorstep ASAP.

 

In these days of more competition, retailers are becoming very creative about how they manage channels, including cost minimization through the use of distributed inventories and distributed fulfillment. Without a sound data integration strategy and technology, this approach would not be possible.

What I find interesting is that it took the retail vertical such a long time to figure this out. Back in the early days of data integration, it was always confounding to me that those in retail did not take the same or more interest in data integration technology as the other verticals, such as healthcare and finance. Indeed, the benefit is rather obvious, including the ability to increase sales and customer satisfaction.

Now that the data integration cat is out of the bag for those in retail, it’s up to them to figure out their own integration strategy for both their data, and their channels. The good news is, data integration tools are now better and more cost-effective. With the use of cloud-based platforms, even storage and management of the data is much less risky and expensive.

The path to well-integrated retail channels leads directly to more profit. That issue unto itself will drive most retailers to use data integration technology, or perhaps upgrade and rethink existing data integration approaches and mechanisms.

When data integration drives sales, then data integration becomes a priority. That’s certainly the case here.


Blog | Data Analytics | | 3 min read

Maximize Customer Lifetime Value Using Historical Data

Will AI Take Data Analyst Jobs?

Acquiring new customers is expensive and it is generally more cost-effective to sell to existing ones. The customer-lifetime-value concept is straightforward, but many companies struggle with determining how to put it into practice.

Do you actually understand your customers’ buying behavior and motivations and what you can do to influence them? Is this information being channeled into your marketing and product development efforts to help you design and position your products and services that will best serve customers’ needs?

Turn Your Data into Actionable Insights

By measuring and maximizing current and forecasted customer value across products, segments and time periods, you will be better able to design new programs that accentuate your best customers and provide you with a distinct business advantage.

With Actian Vector, you can connect all your data, from account histories and demographics to mobile and social media interactions, and merge these disparate sources with speed and accuracy. You already have a wealth of data that can reveal insights into your customers’ motivations, all you need to do is put the puzzle together.

Use this information to uncover key purchase drivers and understand why someone purchases or rejects your products. Assign customer value scores by correlating which characteristics and behaviors lead to value at various points of time during the future. Optimize outbound marketing to give prominence to your high-value customers.

Customize inbound customer touchpoints by arming call centers with highly personalized customer data. This will all lead you to increase customer lifetime value, improving both customer loyalty and profitability.

Predicting Future Buying Behavior

Historical data won’t give you a crystal ball to peer into the future. Many factors can influence individual customer’s buying behavior and it is impossible to capture and analyze all of them. That doesn’t mean each transaction or customer interaction is unique and independent. Individual customers have preferences, buying behavior and social influences and use various types of environmental cues to determine what they will (and won’t) purchase.

Because humans are creatures of habit, past actions (often visible in historical data) are strong indicators of how customers will behave in the future. Similarly, different customers often demonstrate common behaviors and buying patterns when influenced by similar forces.

By analyzing the historical data of both individual customers and groups of similar customers, you can develop more accurate customer profiles, conduct micro-segmentation, identify sources of influence and model the actions your company can take not only to understand, but also to change customers’ buying behavior.

Obtain Better Insights With More Diverse Data

One of the biggest challenges companies face when analyzing customer data is leveraging data from diverse data sources. Using only one or a few data sources may provide you with multiple points-of-view, but it won’t reveal the holistic perspective of your customers you need to be actually successful.

Integrating more (and diverse) data into your analysis will help you eliminate blind spots, improve the accuracy of your findings and increase the end-results of your marketing efforts. Actian DataConnect is a platform that can help you gather your data-sources where the powerful analytics engine of Actian Vector can then distill them into actionable insights. To learn more, visit www.actian.com/vector.