Empathetic Innovation: The Power of Asking Questions for Collaboration Between Tech and Non-Tech Business Users in the Retail Industry

Promoting a culture of asking questions can be an effective way to promote empathetic innovation between tech and non-tech business users. When people are encouraged to ask questions, they are more likely to seek to understand each other's perspectives, needs, and goals. In a tech and non-tech business environment, promoting a culture of asking questions can help bridge the communication gap between the two groups. Non-tech business users may not have a deep understanding of the technical aspects of a project or product, while tech professionals may not be fully aware of the business objectives or customer needs that the project or product is meant to address.

In the retail industry, data is playing an increasingly important role in everything from supply chain management to customer experience. However, effective collaboration between tech and non-tech business users can be challenging, as the two groups often speak different languages and have different priorities. One way to bridge this gap and promote empathetic innovation is to encourage both groups to ask questions. Here are five scenarios that illustrate how this can work:

When designing a new point-of-sale system, a tech team might be focused on the technical details of the platform, while non-tech business users might be more concerned about how the system will impact customer experience. To promote empathetic innovation, the tech team could ask questions like, "How can we make the system more intuitive for non-technical staff to use?" or "What features could we add to improve transaction speed and accuracy?" Meanwhile, the non-tech business users could ask questions like, "How will the system impact customer wait times?" or "What kind of training will be required for staff to use the new system effectively?"

In the context of e-commerce, a non-tech business user might be concerned about the user experience of an online shopping platform, while a tech team might be more focused on the back-end functionality of the site. To promote empathetic innovation, the non-tech business user could ask questions like, "How can we make the shopping experience more intuitive for customers?" or "What features could we add to make the checkout process smoother?" Meanwhile, the tech team could ask questions like, "How will the site handle peak traffic times?" or "What security measures are in place to protect customer data?"

When developing a new inventory management system, a tech team might be focused on the technical details of the platform, while non-tech business users might be more concerned about how the system will impact product availability and customer satisfaction. To promote empathetic innovation, the tech team could ask questions like, "What features could we add to improve inventory tracking and ordering?" or "How can we integrate the new system with existing supply chain management tools?" Meanwhile, the non-tech business users could ask questions like, "How will the new system impact product availability?" or "What kind of metrics will we use to measure the system's impact on customer satisfaction?"

In the context of customer data analysis, a tech team might be focused on the technical details of data collection and analysis, while non-tech business users might be more concerned about how the data can be used to improve customer experience. To promote empathetic innovation, the tech team could ask questions like, "What kind of data points are most relevant to your needs?" or "How can we ensure data accuracy and privacy?" Meanwhile, the non-tech business users could ask questions like, "How can we use the data to personalize the customer experience?" or "What kind of insights can we gain from the data to inform product development?"

When implementing a new customer loyalty program, a tech team might be focused on the technical details of the program, while non-tech business users might be more concerned about how the program will impact customer retention and satisfaction. To promote empathetic innovation, the tech team could ask questions like, "What kind of features are most appealing to your target audience?" or "How can we ensure the program is easy to use and understand?" Meanwhile, the non-tech business users could ask questions like, "What kind of rewards are most effective in driving customer retention?" or "How will we measure the program's impact on customer satisfaction?"

Let's dive deep into one situation when the IT team of a grocery store chain proposes to design a new dynamic pricing solution for their retail outlets. Here are 20 questions that the tech user could ask the non-tech users and 20 questions the non-tech users could ask the tech users:

20 questions that the tech user should ask the non-tech users:

1. What are the key factors that influence pricing decisions in the grocery industry?
2. How do you typically set prices for different products in the store?
3. How do you balance price changes with maintaining customer loyalty?
4. What are the biggest challenges you face in setting prices for your products?
5. How do you factor in competition when setting prices?
6. What are the most popular products that customers purchase?
7. How do you ensure pricing consistency across all store locations?
8. What are the key performance indicators (KPIs) that you use to measure pricing effectiveness?
9. How do you determine when to offer discounts or promotions?
10. How do you account for seasonality in your pricing strategy?
11. What are the key differences in pricing between products in-store versus online?
12. How do you measure the impact of pricing on sales and profitability?
13. How do you handle pricing for perishable products?
14. How do you manage pricing for products with different expiration dates?
15. What is your approach to pricing for private label or store-brand products?
16. What is your target market and how does that impact pricing decisions?
17. How do you handle pricing for products with variable costs?
18. How do you monitor the effectiveness of your pricing strategy over time?
19. How do you balance the needs of different customer segments with pricing decisions?
20. How do you handle customer complaints about pricing?

20 questions that the non-tech user should ask the tech users:

1. How does the dynamic pricing algorithm work?
2. What data sources does the algorithm use to inform pricing decisions?
3. How accurate is the algorithm in predicting demand and adjusting prices accordingly?
4. How does the algorithm factor in competition when setting prices?
5. How does the algorithm balance the need for profitability with customer loyalty?
6. How does the algorithm account for seasonality in pricing decisions?
7. How does the algorithm handle promotions and discounts?
8. How does the algorithm ensure pricing consistency across all store locations?
9. What are the key assumptions and limitations of the algorithm?
10. How does the algorithm adapt to changes in customer behavior and preferences?
11. How does the algorithm address concerns around pricing transparency and fairness?
12. How does the algorithm handle out-of-stock scenarios?
13. How does the algorithm ensure that prices are not set too high or too low?
14. How does the algorithm handle unexpected events that may impact demand or supply?
15. How does the algorithm address concerns around data privacy and security?
16. How does the algorithm integrate with existing pricing and inventory management systems?
17. How does the algorithm handle the impact of external factors, such as changes in the economy or consumer trends?
18. How does the algorithm balance the needs of different customer segments with pricing decisions?
19. How does the algorithm monitor and evaluate its own effectiveness over time?
20. How does the algorithm handle feedback and input from non-tech users in the pricing decision-making process?

Asking these questions allow both teams to work backwards from what success looks like for this project. Say for example, when the tech user explains to non-tech users “How does the algorithm adapt to changes in customer behavior and preferences?”, the benefits of implementing such solution can be objectively evaluated through setting metrics to measure and calculate the business impact from the business PoV. A potential dialog could run like this,

"In the context of dynamic pricing solution for a grocery chain, the algorithm uses a variety of data sources to learn about customer preferences and behavior. For example, it can analyze customer purchase history, browsing patterns, and even social media activity to better understand their needs and preferences. As new data is collected, the algorithm continuously updates its pricing recommendations to match changing customer behavior and preferences.

The benefit of such a solution is that it allows the grocery chain to stay ahead of the curve and adapt to changes in customer behavior quickly. By adjusting prices based on real-time insights, the grocery chain can optimize revenue and profits, while still providing customers with competitive pricing that aligns with their preferences. Additionally, the algorithm can help reduce waste by identifying products that are likely to expire soon and adjusting their prices to move them off the shelves before they go bad.

To measure the impact of dynamic pricing, the non-tech team could help the tech team to come up with several key metrics, including:

1. Revenue and profit: Measuring the impact of dynamic pricing on overall revenue and profit is the most obvious metric. The grocery chain can compare revenue and profit before and after implementing the solution to see the impact on the bottom line.
2. Price elasticity: The grocery chain can measure the impact of price changes on customer demand by calculating price elasticity. This will help them determine the optimal price point for each product and category.
3. Customer retention: Measuring customer retention is an essential metric for any retail business. By understanding how dynamic pricing affects customer retention, the grocery chain can make adjustments to ensure that their pricing strategy aligns with customer needs and preferences.
4. Waste reduction: As mentioned earlier, dynamic pricing can help reduce waste by identifying products that are likely to expire soon and adjusting their prices to move them off the shelves before they go bad. Measuring the impact of this solution on waste reduction can provide valuable insights into the overall effectiveness of the solution.
5. Competitive advantage: Finally, the grocery chain can measure the impact of dynamic pricing on their competitive advantage. By staying ahead of the curve and adapting to changing customer behavior, the grocery chain can maintain a competitive edge over other retailers in the industry."

So you may be curious how then by asking questions, cross-teams can collaborate to speed up the company's innovation. Using the same example, “How does the algorithm adapt to changes in customer behavior and preferences?”, in order for the company to come up with a competitive dynamic pricing model, now the data science team, IT team, operation team and marketing team can sit down together, based on each party's domain experience, to explore and prioritize variables that the data science team can use for feature engineering to measure relevant business impact of dynamic pricing, amplifying the business creativity with domain expertise:

1. Product attributes: Product attributes such as category, brand, size, and quality can impact sales and prices. The data science team may use these attributes to segment products and develop different pricing strategies for each segment.
2. Customer attributes: Customer attributes such as demographics, location, and purchasing behavior can help identify different customer segments with different pricing sensitivities. The data science team may use these attributes to personalize pricing strategies for different customer segments.
3. Seasonality: Sales and prices can vary by season, with higher demand and prices during peak seasons. The data science team may use seasonality variables to adjust prices and predict future sales and revenue.
4. Competitor pricing: Competitor pricing can impact sales and prices for the grocery business. The data science team may use competitor pricing data to adjust prices and develop competitive pricing strategies.
5. Marketing campaigns: Marketing campaigns such as promotions and discounts can impact sales and prices. The data science team may use marketing campaign data to identify the impact of promotions and discounts on sales and revenue.
6. Economic factors: Economic factors such as inflation, unemployment, and consumer confidence can impact sales and prices. The data science team may use economic data to adjust prices and predict future sales and revenue.
7. Time of day/week other event-based factors like weather or epidemic: Sales and prices can vary by time of day or week. The data science team may use time variables to adjust prices and develop time-based pricing strategies.

Now if the teams are ready to up the game, business users are encouraged to be involved in verifying, testing and refining dynamic pricing model. After all, innovation is indeed an iterative process. Let's say the data science team is very progressive in adopting reinforcement learning to train the dynamic pricing model. How could involving business users make a difference? First the data team needs to enable the business team to understand what is reinforcement learning - Suppose we have two dynamic pricing models for a grocery store chain: one that has gone through reinforcement learning and one that hasn't. After a few months of using the models, we observe the following outcomes:

• Model without reinforcement learning: The model is making pricing decisions based on a set of initial rules and doesn't adapt to changes in customer behavior or market conditions. As a result, the model's pricing decisions are not optimized for revenue or profitability. The grocery store chain sees a modest increase in revenue, but the profit margins are lower than expected due to inefficient pricing decisions.
• Model with reinforcement learning: The model has been trained using reinforcement learning and has learned to adjust prices based on customer behavior and market conditions. As a result, the model's pricing decisions are optimized for revenue and profitability. The grocery store chain sees a significant increase in revenue and profit margins due to more effective pricing decisions.

In addition to coming up with variables listed above, the business user's role in training a reinforcement learning model is then critical to ensure that the model is aligned with the business objectives and that it learns to optimize for the right metrics. Some key roles the business users can play are:

1. Define the reward function: The reward function is a crucial component of a reinforcement learning model, as it determines the objective that the model is trying to optimize. The business user should work with the data science team to define the reward function in a way that aligns with the business objectives, such as maximizing revenue, profit margin, or customer satisfaction.
2. Provide feedback on model performance: As the model is being trained, the business user should provide feedback on its performance and suggest changes if necessary. This feedback can help ensure that the model is aligned with the business objectives and that it is making decisions that are in line with business policies and regulations.
3. Monitor the model in production: Once the model is deployed in production, the business user should monitor its performance and ensure that it is continuing to optimize for the desired metrics. If any issues arise, the business user should work with the data science team to address them promptly.

As this example shows, by encouraging questions, tech and non-tech business users can better understand each other's needs and work collaboratively to find solutions that meet both technical and business requirements. This can lead to more empathetic innovation, where both groups are able to contribute their unique perspectives and expertise to create solutions that are both technically sound and meet the needs of the business and customers.

Moreover, when people are encouraged to ask questions, they are more likely to feel heard and valued. This can help build trust and collaboration between tech and non-tech business users, which can lead to better outcomes for everyone involved.