Posts in Behavioral Data

Retailers Can Have Their Bricks and Click Them Too

August 30th, 2017 Posted by Analytics, Behavioral Data 0 thoughts on “Retailers Can Have Their Bricks and Click Them Too”

Despite what you might have read, retail is not dying. Sure, brick-and-mortar retailers today face significant competition from their digital counterparts. But with the right omni-channel strategy, they can outperform online-only stores by providing the best of both worlds – the convenience of online e-commerce along with the human experience of physical stores.

Last week, Macy’s announced its new President will be Hal Lawton, a former eBay and Home Depot executive who is credited with building Home Depot’s stellar interconnected retail experience. Macy’s knows that the path to sustainability involves a unified online and in-store strategy and it has plans to expand its data analytics and consumer insights.

That’s because today retail stores are sitting on an enormous mound of customer and enterprise data, which includes point-of-sale receipts, online visits and purchases, warehouse inventory, and so on. And all of these data points are extremely valuable with the right data analytics strategy and technology in place.

In particular, predictive behavioral analytics has allowed retailers to know when to do what and where. As a result, a store can maintain optimal inventory levels and anticipate what a customer will want to look at on their next visit. It can also pair up a customer’s in-store and online activities to ensure a seamless customer experience and optimal conversion rate with each visit. Imagine, a sales representative having the most up-to-date customer information at their fingertips to help the customer determine the next best action.

It is these kinds of capabilities that will allow companies to stay relevant and win big.

If any of this resonates with you, tweet at me or email me to share your thoughts and experience with analytics.


Data That Stays Together, Works Together

July 31st, 2017 Posted by Analytics, Behavioral Data, Strategy 0 thoughts on “Data That Stays Together, Works Together”

When it comes to data analytics, marketers are missing the forest for the trees. If you think your company’s most marketable data source lies in your enterprise data, think again. Your company is sitting on a gold mine of customer data, siloed in different departments, just waiting to be integrated and activated.

According to eConsultancy’s 2017 Digital Intelligence Briefing on Digital Trends, 59% of marketers who have an intermediate or advanced understanding of the customer journey stated that they had trouble unifying different data sources.

On the front end, you may have your clickstream data, which can include activity from ad displays, social media and email campaigns. Some companies even have data on the voices of their customers and that’s a real trove for piecing together customer demographic profiles. Companies also have loads of data on the back end, waiting to be mined, and this includes margin data, CRM product data, and enterprise resource planning, among others. Combined, both front end and back end data can turbocharge your data analytics system.

But in order for this to happen, the data needs to be removed from its silo and made accessible in a central behavioral data repository. Everything under one roof and one program to rule them all.

A predictive behavioral analytics platform can take almost any type of data sitting in your data lake and turn it into gold. It does not require you to manually identify every single data point across different departments because machine learning algorithms do the work for you. An individual data point teaches the model something completely new, regardless if it is tied to other data points in the dataset or not. As the number of data points coming in to the central behavioral data repository grows, the algorithm’s predictions on user behaviors become more and more accurate. Therefore, companies which have started activating their data are gaining the edge needed to secure their spot as a market leader for tomorrow. The sooner you “compound”, the greater the benefit.

Are you already doing something similar? Tweet at me or email me to share your experiences.


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One Man’s Trash is Another Man’s Data Trove

June 28th, 2017 Posted by Behavioral Data 0 thoughts on “One Man’s Trash is Another Man’s Data Trove”

Companies have been hoarding too much data on consumer demographics. There is an over reliance on demographic data for consumer insights and business decisions. Luckily, a select group of companies are quickly realizing that with the advent of predictive analytics, anonymous and behavioral data can shed more insight and return higher conversions. They’re finding that it’s behavior which best predicts customer intent, and not age, gender, or location.

A single, anonymous visit to a website can provide sufficient insight about the entity in order to identify, segment and predict their propensity for a particular outcome. Entities exhibit tendencies and behaviors of intent, regardless of their age or where they come from. In much the same way that you can catch a thief based on their past behavior, you can also “catch” an intent by analyzing past behavior.

Algorithmic modeling can capture behavioral data and treat each data point like a unique thumbprint, representing the unique sum of all outcomes carried out by that one entity across time. This kind of model is not only precise, but it also gets better with time. The more behaviors that get mapped against intent, the more accurately the model can identify the propensity for intent and outcomes, and deliver the next best offer at the appropriate time.

So embrace your anonymous data. It’s worth so much more now than ever before. If that’s not spinning anonymous trash into golden data, I don’t know what is.

Do you have a question or comment?  Tweet at me or email me.

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