contextual marketing

12月 022021

It’s a hard time to be a decision maker. Unexpected externalities like global pandemics, natural disasters and climate change make it harder to predict – and react to – everyday events. And that’s not just true for the world around us. The organizations we work within are more complex than ever, too.

The volume of communications and channels where we must meet customers and employees has grown exponentially – demanding our attention and reducing our focus. Not to mention added organizational complexity blurring the lines of roles and responsibilities according to geography, product and function.

Gaining control of such complexity requires rapid, streamlined and agile decision making. Technology that enables decision making needs to identify problems and take corrective action in real time to move quickly from questions to decisions.

SAS and Microsoft empower you to make better, faster decisions with unique enterprise decision management with SAS Intelligent Decisioning and Microsoft Power Automate using the SAS Decisioning connector – giving you the ability to design, deploy and manage automated decisions to improve the customer, employee and partner experience.

Enterprise decision management from SAS and Microsoft allows you to automate with a deliberate focus on decisions. You can combine business rules management with digital process automation and ModelOps, including model management and analytics, to accelerate the decision making process.

Together, Intelligent Decisioning and Power Automate unlock a breadth of use cases across the enterprise, including:

  • Insurance: Claims processing. Improve customer satisfaction and process claims faster. Receive insurance claims via Microsoft Power Apps and use Microsoft Power Automate to seamlessly ingest the claim into SAS Intelligent Decisioning. Using neural network models, SAS Intelligent Decisioning can analyze images of damage and compare with policies. If more information is required, Power Automate can trigger a flow to connect with a representative in Dynamics 365 Omnichannel for Customer Service. Once the decision is rendered, Power Automate can trigger process flows to notify the customer and deposit money into the bank account on file.
  • Banking: Credit decisioning. Reduce lender risk, improve decisioning response times and increase your bottom line. Build risk profiles in SAS Intelligent Decisioning by creating score cards and decision tables based off external data points, such as credit score, that assign each customer a risk rating. Use risk ratings to render decisions like home equity and line of credit approvals, and determine the loan amount. Once a decision has been made Power Automate flows can be used to communicate the loan amount to the customer and help them complete the loan agreement.
  • Retail/Banking: Fraud detection. Enable more secure transactions, reduce losses due to fraud and improve customer trust in your organization. SAS Intelligent Decisioning can identify fraudulent purchases and determine an appropriate course of action based on the level of confidence that a purchase is fraudulent. Power Automate can trigger automated reactions like alerting associated parties, denying a purchase at the point of sale, alerting the vendor, or sending notifications to the card holder.
  • Retail: Contextual Marketing. Increase marketing influence and become more customer centric by curating relevant and timely offers based on individual preferences. Use SAS Intelligent Decisioning to build a profile of tastes and preferences via geolocation, recommendation engines and market basket analysis. Use this profile to trigger Power Automate flows to send specific offers that align with important events, like birthdays or anniversaries, and send emails or push notifications to customers with unique, context-specific offers.

To learn more about what SAS Intelligent Decisioning and Microsoft Power Automate can help you achieve, visit

4 ways to make better, faster decisions with enterprise decision management from SAS Viya on Azure was published on SAS Users.

4月 132016

All of us we have had the unfortunate experience of going to a store and encountering a salesperson who is unable to give us expert advice about a product or service. It’s not because salesperson is unwilling to help us, but rather because he or she does not know in enough about the products the company sells.

In fact, the Retail in Belgium survey carried out by Vlerick Business School and Insites Consulting revealed that "not less than 44 percent of consumers feel they know more than the seller about [a] product, after searching online information.” I’m sure these numbers are similar in other countries.

Another challenge for in-store sales is managing the peaks and uneven shopping traffic throughout the day, week or year while having enough competent salespeople for floor coverage.

Are brick-and-mortar operators losing ground over what was considered their strength to ecommerce – expert knowledge? Fortunately, it’s not a bleak as it appears. Traditional retailers have options, but they must be innovative and aggressive to challenge current trends.

Data management and analytics to the rescue

We know that shoppers often use the web to research products before visiting a store, but how can retailers add value once shoppers enter the store? That's where contextual marketing play an important role.Albert pic

Recent technologies such as iBeacons and wi-fi tracking enable retailers to recognize and accurately locate customers as they travel through a store. The next step is persuading a customer to download and enable an app that allows a retailer to better understand individual shopping behavior. The enticement for shoppers is that retailers can make in-store offers in real time via push notifications.

For price-conscious shoppers, downloading an app means they won’t miss out on any promotions. For others, the incentive is learning about new products or offering an improved shopping experience. Being able to meet these goals requires detailed knowledge of individual buying motives. Customer segmentation and creating a customer typology will help.

Once your tracking strategies are enabled, centralized data management for phygitals, both physical and digital data sources, will improve the level of service. The app enables a store to recognize when a shopper enters the store, and using the customer’s transactional history and other data (such as recent online research on the company website), the retailer can improve and enhance the customer experience.

For example, using recommendation engines, you can propose related products based on its location in the store. And all this in real time please because in this context, "right time is real time." If you want to know more about how predictive analytics makes offers more relevant, do not hesitate any longer and read the excellent blog post written by my colleague Adrian Carr about the topic.

The application can also be used by customers to request product/service assistance during their store visit. The sales staff can quickly access information about the customer to better respond to their inquiries.

Needless to say, a customer needs to have a positive initial experience with the app or it will become just another unused app on their smartphone.

If you want to find more information about how SAS enhances the customer experience through contextual marketing, have a look at SAS® Real-Time Decision Manager.  You can also look at our Customer Decision Hub approach to managing customer interactions across all channels.

tags: contextual marketing, customer decision hub, customer experience, customer intelligence, customer segmentation, phygitals, real-time decisioning, retail

The role of contextual marketing for in-store sales was published on Customer Intelligence.