You already know that data is a gateway for retailers to improve customer experiences and increase sales. Through traditional analysis, we’ve been able to combine a customer’s purchase history with their browser behavior and email open rates to help pinpoint their current preferences and meet their precise future needs. Yet the new wave of buzzwords such as “machine learning” and “AI” promise greater accuracy and personalization in your forecasts and the marketing actions they inform.
What distinguishes the latest predictive analytics technology from the traditional analytics approach? Here are three of the numerous examples of this technology’s impact on addressing retail challenges and achieving substantial ROI.
Machine learning has revolutionized various industries, and the retail sector is no exception. With the abundance of data generated by retailers, machine learning algorithms can extract valuable insights, improve decision-making processes, and enhance overall operational efficiency.
Benefits of Machine Learning for Retail
Here are some key benefits of machine learning for the retail industry:
Personalized Customer Experience: Machine learning algorithms can analyze customer data, including purchase history, browsing behavior, and demographics, to create personalized recommendations. By understanding individual preferences and patterns, retailers can offer tailored product suggestions, personalized marketing campaigns, and targeted promotions, leading to improved customer satisfaction and increased sales.
Demand Forecasting: Accurate demand forecasting is crucial for effective inventory management and ensuring product availability. Machine learning models can analyze historical sales data, market trends, seasonal patterns, and external factors to predict future demand with higher accuracy. This enables retailers to optimize inventory levels, reduce out-of-stock situations, minimize excess inventory, and improve overall supply chain efficiency.
Pricing Optimization: Machine learning algorithms can analyze market dynamics, competitor pricing, customer behavior, and other relevant data to optimize pricing strategies. Retailers can dynamically adjust prices based on factors such as demand, inventory levels, and competitive landscape. This helps maximize revenue, increase profit margins, and respond quickly to market changes.
Fraud Detection and Prevention: Retailers often face the challenge of fraud, including online payment fraud, identity theft, and counterfeit products. Machine learning algorithms can analyze vast amounts of transactional data in real-time to detect fraudulent patterns, anomalies, and suspicious activities. This proactive approach enables retailers to mitigate fraud risks, protect customer data, and maintain a secure and trusted shopping environment.
Supply Chain Optimization: Machine learning can optimize various aspects of the supply chain, including demand forecasting, inventory management, logistics, and delivery routes. By analyzing data from multiple sources, including suppliers, warehouses, and transportation systems, machine learning algorithms can identify bottlenecks, streamline operations, reduce costs, and enhance overall supply chain efficiency.
Customer Sentiment Analysis: Machine learning techniques can analyze customer feedback, reviews, and social media data to understand customer sentiment towards products, brands, or the overall shopping experience. Retailers can gain valuable insights into customer preferences, identify areas for improvement, and take proactive measures to enhance customer satisfaction and loyalty.
Increase customer lifetime value: Repeat customers contribute to 40% of a brand’s revenue. But how do you know where to invest your marketing dollars to increase your customer return rate? All of this comes down to predicting which customers are most likely to return and factors that influence the highest customer lifetime value (CLV) for these customers, which are both great use cases for machine learning.
Consider this example: Your customer is purchasing a 4K HD TV and you want to predict future purchases. Will this customer want HD accessories, gaming systems, or an upgraded TV in the near future? If they are forecasted to buy more, which approach will work to increase their chances of making the purchase through you? Predictive analytics can provide the answer.
One of the primary opportunities is to create more personalized sales process without mind-boggling manual effort. The sophistication of machine learning algorithms allows you to quickly review large inputs on purchase histories, internet and social media behavior, customer feedback, production costs, product specifications, market research, and other data sources with accuracy.
Historically, data science teams had to run one machine-learning algorithm at a time. Now, modern solutions from providers like DataRobot allows a user to run hundreds of algorithms at once and even identify the most applicable ones. This vastly increases the time-to-market and focuses your expensive data science team’s hours on interpreting results rather than just laying groundwork for the real work to begin.
Attract new customers: Retailers cannot depend on customer loyalty alone. HubSpot finds that consumer loyalty is eroding, with 55% of customers no longer trusting the companies they buy from. With long-running customers more susceptible to your competitors, it’s important to always expand your base. However, as new and established businesses vie for the same customer base, it also appears that customer acquisition costs have risen 50% in five years.
Machine learning tools like programmatic advertising offer a significant advantage. For those unfamiliar with the term, programmatic advertising is the automated buying and selling of digital ad space using intricate analytics. For example, if your business is attempting to target new customers, the algorithms within this tool can analyze data from your current customer segments, page context, and optimal viewing time to push a targeted ad to a prospect at the right moment.
Additionally, businesses are testing out propensity modeling to target consumers with the highest likelihood of customer conversion. Machine learning tools can score consumers in real time using data from CRMs, social media, e-commerce platforms, and other sources to identify the most promising customers. From there, your business can personalize their experience to better shepherd them through the sales funnel – even going as far as reducing cart abandon rates.
Automate touch points: Often, machine learning is depicted as a way to eliminate a human workforce. But that’s a mischaracterization. Its greatest potential lies in augmenting your top performers, helping them automate routine processes to free up their time for creative projects or in-depth problem-solving.
For example, you can predict customer churn based on irregularities in buying behavior. Let’s say that a customer who regularly makes purchases every six weeks lapses from their routine for 12 weeks. A machine learning model can identify if their behavior is indicative of churn and flag customers likely not to return. Retailers can then layer these predictions with automated touch points such as sending a reminder about the customer’s favorite product – maybe even with a coupon – straight to their email to incentivize them to return.
How to Get Started
In summary, machine learning offers a range of benefits for the retail industry, including personalized customer experiences, accurate demand forecasting, pricing optimization, fraud detection, supply chain optimization, and customer sentiment analysis. By leveraging the power of machine learning, retailers can gain a competitive edge, drive growth, and deliver exceptional shopping experiences to their customers.
Though implementing machine learning can transform your business in many ways, your data needs to be in the right state before you can take action. That involves identifying a single customer across platforms, cleaning up the quality of your data, and identifying specific use cases for machine learning. With the right partner, you can not only make those preparations but rapidly reap the rewards of powering predictive analytics with machine learning.
Want to learn how the 2nd Watch team can apply machine learning to your business? Contact us now.
Data sharing has become more complex, both in its application and our relationship to it. There is a tension between the need for personalization and the need for privacy. Businesses must share data to be effective and ultimately provide tailored customer experiences. However, legislation and practices regarding data privacy have tightened, and data sharing is tougher and fraught with greater compliance constraints than ever before. The challenge for enterprises is reconciling the increased demand for data with increased data protection.
The modern world runs on data. Companies share data to facilitate their daily operations. Data distribution occurs between business departments and external third parties. Even something as innocuous as exchanging Microsoft Excel and Google Sheets spreadsheets is data sharing!
Data collaboration is entrenched in our business processes. Therefore, rather than avoiding it, we must find the tools and frameworks to support secure and privacy-compliant data sharing. So how do we govern the flow of sensitive information from our data platforms to other parties?
The answer: data clean rooms. Data clean rooms are the modern vehicle for various data sharing and data governance workflows. Across industries – including media and entertainment, advertising, insurance, private equity, and more – a data clean room can be the difference-maker in your data insights.
There is a classic thought experiment wherein two millionaires want to find out who is richer without actually sharing how much money they are individually worth. The data clean room solves this issue by allowing parties to ask approved questions, which require external data to answer, without actually sharing the sensitive information itself!
In other words, a data clean room is a framework that allows two parties to securely share and analyze data by granting both parties control over when, where, and how said data is used. The parties involved can pool together data in a secure environment that protects private details. With data clean rooms, brands can access crucial and much-needed information while maintaining compliance with data privacy policies.
Data clean rooms have been around for about five years with Google being the first company to launch a data clean room solution (Google Ads Data Hub) in 2017. The era of user privacy kicked off in 2018 when data protection and privacy became law, most notably with the General Data Protection Regulation (GDPR).
This was a huge shake-up for most brands. Businesses had to adapt their data collection and sharing models to operate within the scope of the new legislation and the walled gardens that became popular amongst all tech giants. With user privacy becoming a priority, data sharing has become stricter and more scrutinized, which makes marketing campaign measurements and optimizations in the customer journey more difficult than ever before.
Data clean rooms are crucial for brands navigating the era of consumer protection and privacy. Brands can still gain meaningful marketing insights and operate within data privacy laws in a data clean room.
Data clean rooms work because the parties involved have full control over their data. Each party agrees upon access, availability, and data usage, while a trusted data clean room offering oversees data governance. This yields the secure framework needed to ensure that one party cannot access the other’s data and upholds the foundational rule that individual, or user-level data cannot be shared between different parties without consent.
Personally, identifying information (PII) remains anonymized and is processed and stored in a way that is not exposed to any parties involved. Thus, data sharing within a data clean room complies with privacy policies, such as GDPR and California Consumer Privacy Act (CCPA).
How does a data clean room work?
Let’s take a deeper dive into the functionality of a data clean room. Four components are involved with a data clean room:
#1 – Data ingestion
Data is funneled into the data clean room. This can be first-party data (generated from websites, applications, CRMs, etc.) or second-party data from collaborating parties (such as ad networks, partners, publishers, etc.)
#2 – Connection and enrichment
The ingested data sets are matched at the user level. Tools like third-party data enrichment complement the data sets.
#3 – Analytics
The data is analyzed to determine if there are intersections/overlaps, measurement/attribution, and propensity scoring. Data will only be shared where the data points intersect between the two parties.
#4 – Application
Once the data has finished its data clean room journey, each party will have aggregated data outputs. It creates the necessary business insights to accomplish crucial tasks such as optimizing the customer experience, performing reach and frequency measurements, building effective cross-platform journeys, and conducting deep marketing campaign analyses.
What are the benefits of a data clean room?
Data clean rooms can benefit businesses in any industry, including media, retail, and advertising. In summary, data clean rooms are beneficial for the following reasons:
You can enrich your partner’s data set.
With data clean rooms, you can collaborate with your partners to produce and consume data regarding overlapping customers. You can pool common customer data with your partners, find the intersection between your business and your partners, and share the data upstream without sharing sensitive information with competitors. An example would be sharing demand and sales information with an advertising partner for better-targeted marketing campaigns.
You can create governance within your enterprise.
Data clean rooms provide the framework to achieve the elusive “single source of truth.” You can create a golden record encompassing all the data in every system of records within your organization. This includes sensitive PII such as social security numbers, passport numbers, financial account numbers, transactional data, etc.
You can remain policy compliant.
In a data clean room environment, you can monitor where the data lives, who has access to it, and how it is used with a data clean room. Think of it as an automated middleman that validates requests for data. This allows you to share data and remain compliant with all the important acronyms: GDPR, HIPPA, CCPA, FCRA, ECPA, etc.
But you have to do it right…
With every data security and analytics initiative, there is a set of risks if the implementation is not done correctly. A truly “clean” data clean room will allow you to unlock data for your users while remaining privacy compliant. You can maintain role-based access, tokenized columns, and row-level security – which typically lock down particular data objects – and share these sensitive data sets quickly and in a governed way. Data clean rooms satisfy the need for efficient access and the need for the data producer to limit the consumer to relevant information for their use case.
Of course, there are consequences if your data clean room is actually “dirty.” Your data must be federated, and you need clarity on how your data is stored. The consequences are messy if your room is dirty. You risk:
Loss of customer trust
Fines from government agencies
Inadvertently oversharing proprietary information
Locking out valuable data requests due to a lack of process
Despite the potential risks of utilizing a data clean room, it is the most promising solution to the challenges of data-sharing in a privacy-compliant way.
To get the most out of your data, your business needs to create secure processes to share data and decentralize your analytics. This means pooling together common data with your partners and distributing the work to create value for all parties involved.
However, you must govern your data. It is imperative to treat your data like an asset, especially in the era of user privacy and data protection. With data clean rooms, you can reconcile the need for data collaboration with the need for data ownership and privacy.
2nd Watch can be your data clean room guide, helping you to establish a data mesh that enables sharing and analyzing distributed pools of data, all while maintaining centralized governance. Schedule time to get started with a data clean room.