The pressure is on for retail businesses to grow without relying on price increases. Snowflake’s 2026 retail analysis indicates a shift toward like-for-like volume growth through productivity gains.
At the same time, Attentive’s survey of 1,050 shoppers found 73% are more likely to buy when product recommendations reflect their needs and preferences.
In theory, predictive analytics should help retailers close in on what customers need to get them to convert. But IDC’s 2025 retail technology research shows that data visibility, synchronization, and unification remain competitive challenges.
Ahead, we’ll walk through the top predictive analytics challenges blocking ecommerce growth and how to fix each one, starting with the data feeding the model in the first place.
What predictive analytics challenges look like in ecommerce
The purpose of predictive analytics is to predict what’s likely to occur next based on historical and current data. For ecommerce businesses, that’s typically which products are likely to sell, which customers will churn, or where demand will rise.
But a prediction can lose value at any point between the model and the eventual business decision.
A few warning signs include:
- The forecast is wrong. A model predicts a demand spike that never arrives, leaving you with excess inventory.
- The teams see different answers. Your ecommerce, merchandising, and inventory reports conflict because each system uses different data or definitions.
- The data doesn’t reach the right teams at the right times. The model identifies customers likely to churn, but the insight never reaches the team responsible for retention.
- The action underperforms. A promising customer segment receives a campaign, but fragmented profiles or inconsistent execution weaken the result.
IDC’s report says retailers already generate large volumes of data across ecommerce, point of sale (POS), loyalty programs, enterprise resource planning (ERP), and supply chain systems, but much of it remains fragmented, delayed, inconsistent, or difficult to govern.
Patrick Joyce, VP of engineering at Shopify, calls this the “fragmentation tax.” These inefficiencies, Patrick says, compound over time and impact “not just the IT budget, but the entire organization's ability to compete effectively in modern retail.”
The handoff from insight to action can break down, too. Dynamic Yield by Mastercard found that 67% of surveyed ecommerce brands lacked a unified audience strategy. Another 17% looked for significant insights in personalization test data but did nothing with what they found.
Take Astrid & Miyu. Before migrating from Adobe Commerce to Shopify, the jewelry brand’s customer data was fragmented across platforms, limiting personalization across their online and physical stores.
Shopify gave the team one view of customer search, browsing, purchase, and loyalty activity across channels. Since adopting the platform’s unified commerce model, Astrid & Miyu has seen a fivefold increase in customers making four or more purchases. Plus, repurchasing customers who shop both online and in store also have a 40% higher customer lifetime value (CLV) than online-only customers.
“Shopify’s big singular view of our customer is the secret power to scaling fast and managing international growth,” says Molly Allen, senior ecommerce manager.
Challenge #1: Poor quality data
When you have bad data, it moves downstream—into the forecast, the campaign, and the customer's inbox, and it gets more expensive at every step.
Stibo Systems' 2025 research found 55% of business leaders have lost revenue or missed opportunities because of poor customer data quality, and 82% of those put the loss above $100,000 a year.
Neda Nia, chief product and growth officer at Stibo Systems, describes poor dataas a “negative multiplier” for trust, in that it reduces confidence in internal decisions while degrading the experiences customers receive.
For example, 32% of those surveyed have run a marketing campaign that targeted the wrong audience because the underlying data was wrong, and a third named inconsistent data as their top obstacle to meeting customer expectations.
Most of this is preventable and unaddressed at the same time. For instance, 58% of organizations don't use automated data-cleansing tools, and 61% skip third-party verification entirely. That's not a resourcing gap so much as a governance one.
Much of that mess comes from data living in too many disconnected places to begin with. But retailers running unified commerce instead of a patchwork of solutions, implementing one system for POS, ecommerce, and customer data, cut out an entire category of the problem before it starts.
In fact, independent research found that brands already using Shopify's unified commerce features report 150% omnichannel gross merchandise value (GMV) growth and 22% lower total cost of ownership (TCO) as stock and pricing remain in sync across every channel.
Challenge #2: Data privacy and security risks
A better prediction typically requires more data. You might combine a customer’s browsing history, purchases, returns, loyalty activity, location, and customer service interactions to predict what they will buy or whether they are likely to churn.
But that richer customer profile is also more sensitive to threats. For example, in June 2026, South Korea’s Personal Information Protection Commission fined the ecommerce company Coupang $409 million for violations related to a breach that affected more than 33 million customers.
Plus, security threats can even distort the predictions themselves. Thales found that bots generated 53% of traffic to retail websites in 2025, with bad bots accounting for 39%. These bots can take over customer accounts, scrape prices, hoard limited inventory, test stolen credentials, and attack APIs.
Meanwhile, the regulatory net keeps widening. In the US, Indiana, Kentucky, and Rhode Island all brought new consumer privacy laws into effect for 2026, extending compliance obligations to B2B sellers.
This creates two related responsibilities for retailers. The first is to protect the data from unauthorized use, and the second is to prove that your business has permission to use it.
Shopify gives you native controls over who and what can access commerce data. You can create custom roles that limit employees to the areas of the Shopify admin they need, with additional permissions required for sensitive customer information.
In markets where consent is required, web pixels run only after the customer grants the relevant permissions. You can also request or erase customer data in response to valid privacy requests.
Pro tip: Read more about Shopify’s security policy, including adherence to PCI, SOC, and GDPR requirements.
Challenge #3: Lack of skills, ownership, and change management
A predictive model can be factually correct and still change nothing if nobody in the team knows how to read it or feels responsible for acting on it.
DataCamp's 2026 survey of over 500 enterprise leaders found 59% report an AI skills gap, and only 35% have a mature, organization-wide AI literacy program. And leaders at organizations that do have mature programs are roughly twice as likely to report significant return on investment (ROI) for their AI initiatives ROI (42% versus 21%), and half as likely to report none at all.
Further, Pluralsight surveyed 600 technology decision-makers in 2025 and found that 75% of companies had delayed or paused at least one AI project because employees lacked AI expertise.
Shopify can make predictive insights easier for ecommerce teams. Sidekick, Shopify's built-in AI assistant, works within the context of a store regardless of technical skill. Sidekick lets your employees ask questions about store performance in plain language, then translates those questions into ShopifyQL reports and visualizations.
While it doesn't solve for organizational ownership on its own, it does enable interpretation to happen at the point of the question.
Apparel retailer Jaded London, for example, manages seven international markets, but their head of ecommerce, Jamie Evans, was receiving 10 to 15 analytics requests each week.
Now with Shopify Sidekick, their employees can ask those questions in plain language. The merchandising team no longer has to wait for Jamie to pull routine reports, while city-level analysis that once took days can now be completed in minutes.
Jaded London says the change saves 10 to 15 hours each week, and has helped inform decisions such as where to hold international pop-ups.
“I literally used Sidekick the other day to analyze potential markets...the data helps us make faster, more confident decisions,” says Jamie.
Challenge #4: Proving ROI and measuring business impact
Most retailers believe AI is working, and most of them can't actually show their math.
NVIDIA's 2026 “State of AI in Retail and CPG” survey found 89% of respondents say AI has increased annual revenue, and 95% say it's decreased annual costs. Those are the kind of numbers that can get a project renewed.
But Gartner's CDAO survey of data and analytics leaders found that 30% name the inability to measure data, analytics, and AI impact on business outcomes as their single biggest challenge. Further, only 22% have defined, tracked, and communicated business impact metrics for most of their initiatives.
McKinsey recommends measuring AI across five connected layers: technical performance, user adoption, operational KPIs, strategic outcomes, and financial impact.
Shopify Analytics can help you track the commerce outcomes on the other end of the prediction. Your teams can customize reports, compare results with previous periods or targets, analyze customer cohorts, and use ShopifyQL to examine the metrics most relevant to the use case.
Before switching to ShopifyQL Notebooks, outdoor apparel brand Decathlon was trying a new market strategy in the US. To analyze its effectiveness, they relied on a standard process of exporting to Google Sheets and producing a static report to hand to leadership. This process meant they’d gain no additional insights until the next time they repeated the time-consuming process.
In addition to refreshing data in a live environment, comparing key performance indicators (KPIs) in one report, and going back and reviewing earlier decisions and budget investments, ShopifyQL Notebooks enabled Decathlon to make more informed business decisions. Their reporting became 50% faster, while ready-to-use templates reduced analysis time by 60%.
“We can remember what has been done in the past, adding, for example, decisions that have been made or budget that was invested, and easily revisit the data so we can always keep track of important patterns regardless of what we remember or if certain employees have moved on,” says Tony Leon, chief technology officer.
Challenge #5: Weak governance, bias, and explainability
A predictive model doesn’t need permission to produce an answer. Your business needs rules for what happens next.
But most organizations aren’t there yet. Cisco’s 2026 “Data Privacy Benchmark Study” found that three-quarters of surveyed organizations had established an AI-governance body, but only 12% described it as mature and proactive.
Similarly, bias doesn’t always arrive as an obviously discriminatory output; it can begin with an incomplete view of the business. For example, a customer who primarily shops in-store may appear less valuable to a model trained on ecommerce activity. Or sales of products bought by smaller customer groups may remain underforecast because they represent a smaller share of the training data.
Shopify’s customer segmentation gives teams a more transparent way to put some predictions into practice. The segments are built from visible, rule-based criteria that employees can inspect, test, and change before using them in a campaign.
For example, Shopify’s Predicted spend tier estimates a customer’s future spending potential using their purchase frequency, average order value (AOV), number of orders, and most recent purchase. You can combine that prediction with explicit conditions, such as including only high-potential customers who have consented to receive marketing.
“We’ve had product launches where we’ve used Shopify’s segmentation to just email people that have bought from that brand before,” says Rennie Wood, founder of Wood Wood Toys.
“We pull out the 10% or 20% of the email list and customize the messaging to them, and it performs really well. Those are the ones that have eye-popping conversion numbers.”
For governance, the U.S. National Institute of Standards and Technology (NIST) recommends documenting an AI system’s intended purpose, operating context, roles, responsibilities, risk tolerances, and monitoring processes. They also recommend continually tracking the performance and trustworthiness of both internal and third-party models.
Before putting a predictive model into production, answer:
- What decision will it influence?
- Which data can it use?
- Who owns the resulting action?
- What level of confidence is required?
- When must a human review or override it?
- How will you test results across channels, markets, and customer groups?
- What would cause you to pause or retire the model?
Challenge #6: Scaling from pilot to production
A pilot proves that a model can produce a useful prediction. And production proves that the business can keep delivering and acting on that prediction under real operating conditions.
That second test is much harder. IBM’s 2026 study of 2,000 C-suite executives found that 68% worried their AI efforts would fail because they weren’t integrated with core business activities.
A pilot typically has a narrow use case, a cleaned dataset, and a small group of people watching closely. But production introduces live transactions, changing customer behavior, system outages, delayed inputs, duplicate events, and more teams depending on the result.
Gartner found something similar a year earlier. According to their research, at least 30% of generative AI projects were abandoned after proof of concept, for reasons that were seldom about the model itself; rather, they failed due to poor data quality, inadequate risk controls, escalating costs, unclear business value, or a combination of those factors.
The prediction also has to arrive at the speed of the decision. For instance, a merchandising team may only need a demand forecast once a day, and a fraud or fulfillment prediction may need to influence an order within seconds.
Shopify can help connect predictions to live commerce workflows. Shopify Flow monitors store events and uses triggers, conditions, and actions to automate processes within Shopify and across connected apps.
For custom integrations, Shopify webhooks can send near-real-time notifications when events such as orders, returns, refunds, or inventory changes occur. An external analytics system can use those events to update a prediction or return an insight to the relevant workflow.
To move from pilot to production:
- Start with one defined workflow. Specify the decision the prediction will influence and how quickly the result needs to arrive.
- Test end to end. Use realistic volumes, incomplete records, duplicate events, system delays, and failure scenarios.
- Set fallback rules. Decide whether the existing process continues, the action pauses, or a person reviews the decision when the model is unavailable.
- Roll out gradually. Start with one market, channel, product category, or customer group before expanding the model’s reach.
- Monitor inputs and outcomes. Track data freshness, prediction quality, system reliability, employee overrides, and the eventual business result.
- Create a feedback loop. Feed actual purchases, returns, churn, or inventory outcomes back into the evaluation process.
- Define retraining and retirement thresholds. Agree on how much performance can decline before the model is adjusted, paused, or replaced.
Challenge #7: Turning data into actionable insights
A prediction that never reaches the person who could act on it may as well not exist.
Zipline's 2026 “State of Retail Communication and Execution” report surveyed retail leaders across the US and Canada and found that 70% of the people closest to day-to-day execution have no effective way to report back what's broken.
And 63% say at least 1 in 4 store initiatives fail to land as planned. The communication infrastructure helps explain this, because 81% of retailers still run store communication primarily through email.
Your predictive insights can get lost in much the same way.
Shopify can bring the insight and the action into the same operating environment.
Sidekick can create or edit Shopify Analytics reports from a plain-language request. Once the team decides how it wants to respond, Sidekick can also draft a Shopify Flow workflow from a description of the process.
Flow then monitors for a specific trigger, checks any conditions you’ve set, and takes an approved action. You could, for example, alert the inventory team when stock reaches a defined threshold, or hold an order that Shopify’s fraud analysis classifies as high risk.
Take Maggy London. Their four-person ecommerce team supports six fashion brands and uses Sidekick to analyze sales, customer behavior, search activity, and product performance.
The team noticed an opportunity to keep their onsite merchandising closer to what customers were buying. So, using Sidekick, they created a Shopify Flow that identifies the top 20 bestselling products each week, tags them as “trending,” and removes the tag seven days later.
The same analysis travels beyond ecommerce. Maggy London used Sidekick to build a Q3 buying road map based on historical style performance, recommended quantities, and potential hero products. Their reporting time fell by more than 80%, from three or four hours a week to 20 or 30 minutes. But the more important shift was how Shopify helped the team move from noticing a pattern to changing merchandising and buying decisions while the information was still useful.
“Sidekick turned our ecom team into a strategic intelligence hub for the whole company. The insights we pull don't just improve our website—they inform design, and product development,” says Sara Bako, president.
Predictive analytics challenges FAQ
What are the most common challenges in analytics?
The most common challenges are data quality issues, disconnected data sources, weak data integration, unclear ownership, and difficulty turning data-driven insights into action. Teams also need the right skills to analyze data, maintain predictive analytics models, and monitor whether outputs remain accurate after deploying predictive analytics technology.
What is a downside of predictive analytics?
A model can produce confident but unreliable predictions when it learns from incomplete data, biased historical data, or patterns that no longer reflect current behavior. Also, predictive algorithms can be difficult to explain, and collecting more customer information can introduce privacy and governance risks.
Businesses should ensure data quality, keep people involved in consequential decisions, and monitor deployed models for performance degradation.
What is the future of predictive analytics?
The future of predictive analytics technology involves using fresh data, AI and machine learning algorithms, and automation to update predictions and trigger faster decisions. Predictive analytics capabilities will increasingly sit inside everyday commerce workflows rather than separate dashboards.
However, successful predictive analytics implementation will still require human oversight, ongoing monitoring, and clear business objectives.
What are some examples of predictive analytics?
Common examples include using past data to forecast product demand, identify customers likely to churn, anticipate returns, detect fraud, and recommend products. Shopify’s predicted spend tier, for example, estimates customers’ future spending potential so retailers can create relevant segments. These predictive analytics solutions can improve inventory planning, marketing performance, customer satisfaction, and operational efficiency when teams act on the results.
Which tool is best for predictive analytics?
The best predictive analytics tools depend on your data, skills, and use case. Shopify Analytics and Sidekick suit ecommerce teams that want accessible predictive analysis and reporting within their commerce platform.
More complex predictive analytics implementation may require data scientists using platforms like Amazon SageMaker or Azure Machine Learning to build and deploy custom models using machine learning and data mining techniques. When implementing predictive analytics, prioritize compatibility with your existing systems, explainability, monitoring, and the team’s ability to act on the results.
