Historical data is so outdated

By Ian Nadeau on March 10, 2023

When it comes to ecommerce, history rarely repeats itself. Fashion trends change. Electronics inevitably become obsolete. Fads quickly turn passé. Why then do so many marketers rely heavily on historical data to fuel their efforts? 

Traditionally, organizations have archived consumer information (e.g., past purchases, store visits, and location) to personalize how they target that individual. However, this strategy fails to account for evolving consumer preferences and behaviors — so what was once actionable is now irrelevant. 

“Without a systematic way to start and keep data clean, bad data will happen.”

Donato Diorio, Founder of FLiK

Shifting away from historical data

Ecommerce marketers have long used historic analysis to compare trends year-over-year and review consumer behavior patterns. This data readily identifies popular items, successful marketing channels, top-performing keywords, and more — all valuable insights that help brands see their longitudinal performance. 

The problem with this approach is that every data point on display provides insight into a consumer engagement that is no longer influenceable — be that with promotions, product suggestions, or social proof. If the visit didn’t result in a conversion, marketing now has to find a way to bring that person back to the site. But what if they already found what they were looking for elsewhere? 

Shopping, whether physical or online, is an emotional experience. When someone is caught up in that moment, getting the thing they want is a top priority. Connecting to the consumer at this time is critical to increasing conversions. That is why ecommerce is shifting away from historical data and towards real-time analytics. 

Why real-time analytics?

Real-time analytics involves processing and measuring data as soon as it becomes available. The immediacy of this solution makes it agile enough to flow with the rapid changes in consumer behaviors and trends. Unlike historical data, real-time inputs provide current insights that enable quick reactions — so opportunities can be seized before they’re lost.  

Benefits of real-time analytics include:

  • Time-sensitve data: See what site visitors are actively doing and how they’re behaving to better drive experience decisioning
  • Immediate testing: Ask and answer optimization questions in real-time by actively monitoring how changes affect user experiences  
  • Rapid responses: React to sudden changes to the market, your inventory, or anything else to avoid costly mistakes or take advantage of big opportunities 

In order to be truly effective, real-time analytics need to handle massive amounts of data with little to no query latency — not a simple task. Because of this, many ecommerce brands are actually processing data in near real-time. In near real-time, data is synthesized in minutes, rather than immediately. The technical specifications of running truly real-time analytics are just one of the obstacles brands run into when trying to deploy a solution. 

The challenges of real-time analytics

Real-time analytics depend on correct data to properly function. If the wrong information goes into the system, a ripple effect can occur and invalidate an entire database. The collection and upkeep of consumer data is a challenge for ecommerce. Because of this, marketers are finding it hard to rely on what they have on file. A survey of 964 marketers and marketing data analysts found that 34% of CMOs don’t trust their marketing data, with the percentage rising to more than 50% for CTOs and CDOs. 

Additionally, the majority of ecommerce brands aren’t set up to optimize real-time analytics even if they are able to maintain data accuracy. Only 4% of businesses involved in a survey of 1,600 said that they have the capability to realize the benefits of their data.

Lastly, data latency — the time it takes to make data queryable — for real-time analytics can vary widely depending on what’s powering the system. This means that marketers could be decisioning based on an activity that is now in the consumer’s rearview. 

How can ecommerce marketers overcome the issues of both historical data and real-time analytics? With in-session marketing. 

The in-session difference

Historical data works on past insights, while real-time analytics usually lag minutes behind consumer engagement. In-session marketing instantaneously synthesizes clickstream data using AI to truly understand the intent of every visitor (even anonymous) the moment a site event occurs. 

In-session marketing focuses on the micro-behaviors of active users and identifies the likelihood of purchase based on algorithms using industry-specific models. With predictions to understand who will purchase or not, AI-driven decisioning can automatically deploy tailored actions at scale — all within milliseconds. 

The real magic of in-session marketing happens when a visitor’s intent is determined to be influenceable (can be convinced to purchase). We know shopping is an emotional experience. What if an ecommerce brand could leverage that emotion by providing tailored discounts or promotions based on what the consumer is actively looking at? What about guiding a visitor to things that are in stock based on their activity rather than having them leave after landing on a product that’s unavailable? The opportunities that come from knowing and reacting to customer intent in-session unlock greater revenue outcomes for ecommerce brands — from increasing conversion rates to lowering cart abandonment to smarter incentive offers. 

In-session marketing platforms deliver predictive segmentation of all users (yes, anonymous included), instantly triggered experiences and immediate results. Further, the lack of any data latency with in-session marketing enables ecommerce brands to act on the most viable insights. Poor data costs U.S. businesses $600 billion yearly. Avoid adding to this number by finding a better source of truth than historical data or near real-time analytics. 

Learn more about Session AI’s in-session marketing.

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