Integrating Data Analytics into Consulting Practices

If your CX Program simply consists of surveys, it's like trying to understand the whole movie by watching a single frame. You have to integrate data, insights, and actions if you want to understand how the movie ends, and ultimately be able to write the sequel. But integrating multiple customer signals isn't easy. In fact, it can be overwhelming. I know because I successfully did this in the past, and counsel clients on it today. So, here's a 5-step plan on how to ensure that the integration of diverse customer signals remains insightful and not overwhelming: 1. Set Clear Objectives: Define specific goals for what you want to achieve. Having clear objectives helps in filtering relevant data from the noise. While your goals may be as simple as understanding behavior, think about these objectives in an outcome-based way. For example, 'Reduce Call Volume' or some other business metric is important to consider here. 2. Segment Data Thoughtfully: Break down data into manageable categories based on customer demographics, behavior, or interaction type. This helps in analyzing specific aspects of the customer journey without getting lost in the vastness of data. 3. Prioritize Data Based on Relevance: Not all data is equally important. Based on Step 1, prioritize based on what’s most relevant to your business goals. For example, this might involve focusing more on behavioral data vs demographic data, depending on objectives. 4. Use Smart Data Aggregation Tools: Invest in advanced data aggregation platforms that can collect, sort, and analyze data from various sources. These tools use AI and machine learning to identify patterns and key insights, reducing the noise and complexity. 5. Regular Reviews and Adjustments: Continuously monitor and review the data integration process. Be ready to adjust strategies, tools, or objectives as needed to keep the data manageable and insightful. This isn't a "set-it-and-forget-it" strategy! How are you thinking about integrating data and insights in order to drive meaningful change in your business? Hit me up if you want to chat about it. #customerexperience #data #insights #surveys #ceo #coo #ai

Data professionals often take mature digital infrastructure for granted. Consulting has taught me that a lot of small businesses struggle to master essential digital processes such as their customer journey. You can be eager to implement data infrastructure and solutions due to enterprise experience, but fail to realize it will produce no value right now. That's why I'm giving you 5 takeaways from surfacing $620K in missed revenue for a digitally immature roofing company using a simple dashboard: 𝟭. 𝗔𝗹𝘄𝗮𝘆𝘀 𝗺𝗲𝗲𝘁 𝘁𝗵𝗲 𝗰𝗹𝗶𝗲𝗻𝘁 𝘄𝗵𝗲𝗿𝗲 𝘁𝗵𝗲𝘆 𝗮𝗿𝗲 ↳ Don't implement fancy data tools to look impressive or be excited. Focus on solving their problems. Use tools that are already there for you (ie. Google Sheets, Looker Studio). 𝟮. 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝘇𝗲 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝗮 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗱𝗮𝘁𝗮 𝗺𝗼𝗱𝗲𝗹 𝗲𝗮𝗿𝗹𝘆 𝗼𝗻 ↳ Translating their sales process into the language of data is critical for gaining alignment across the company's departments and teams 𝟯. 𝗞𝗲𝗲𝗽 𝘁𝗵𝗲 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 𝗱𝗲𝘀𝗶𝗴𝗻 𝘀𝗶𝗺𝗽𝗹𝗲 𝗮𝗻𝗱 𝗰𝗼𝗻𝗰𝗶𝘀𝗲𝗹𝘆 𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝘃𝗲 ↳ Use easy to read line and bar charts, but ensure to include secondary visuals that provide additional context they'd find in their source tool 𝟰. 𝗔𝘀𝘀𝘂𝗺𝗲 𝘁𝗵𝗲𝘆 𝗵𝗮𝘃𝗲 𝘇𝗲𝗿𝗼 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗼𝗳 𝗱𝗮𝘁𝗮 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗮𝗻𝗱 𝘁𝗿𝗮𝗶𝗻 𝘁𝗵𝗲𝗺 𝗔𝗦𝗔𝗣 ↳ A lot of small business execs focused on sales lack basic analytics knowledge. Train them promptly after delivery to get them comfortable with usage and adoption. 𝟱. 𝗟𝗮𝘆 𝗼𝘂𝘁 𝗴𝗮𝗽𝘀 𝘆𝗼𝘂 𝘀𝗲𝗲 𝗶𝗻 𝘁𝗵𝗲𝗶𝗿 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 ↳ Even after process improvement exercises, adoption can be tough for sales teams in certain cultures. Always explain gaps in your final product due to source data quality issues. If you're able to focus on these things, you will be well on your way to unlocking the value of data on digitally immature organizations. You don't always need to spend thousands on advanced data infrastructure to drive profit up for companies, especially these kinds of businesses. P.S. How have you driven value with data for digitally immature companies?

Data complexity increases as volume, velocity, and variety expand. Today, most organizations measure more things than they did in the past and struggle to manage all their data. In my #analytics consulting career, I’ve seen data teams approach data complexity in two ways. 1️⃣ 'Pass along’ approach Essentially, analytics teams relay the data complexity to business teams and stakeholders. Over time, more data complexity means more data products and more complicated offerings. 👉 A basic dashboard becomes more detailed with multiple tabs and advanced filters. 👉 A simple 10-page report turns into a 60-page one. 👉 A single access point for customer information expands to five disparate systems. I remember talking to an analytics executive who bragged that his organization had over 20,000 Power BI reports or dashboards. While he might have been impressed by this number, I don’t think the business teams at his organization would have been as enthusiastic. The ‘pass along’ approach deters data adoption rather than encouraging more people to use data. End users become increasingly overwhelmed by the expanding number of increasingly complex data products. This approach is focused on production rather than business outcomes. 2️⃣ ‘Focused and streamlined’ approach These data teams realize a ‘pass along’ approach only transfers the data complexity to business users and doesn’t directly address it. While it may not be possible to offset the increasing data complexity completely, these analytics teams strive to mitigate it as much as possible. They understand data products can be enriched with more or better information, but that doesn’t mean business users should be burdened with excessive amounts of data and increasingly complicated tools. These analytics teams realize they can expand data adoption by offering focused, meaningful information and streamlining how it is delivered. Their goal is to make the data as accessible and useful as possible, not overwhelming or confusing.   Some #data professionals will push back on this optimized approach. They may feel business teams won’t appreciate their ‘behind the curtain’ contributions to making data easy to access and use. I disagree. When you streamline the ability for business teams to access relevant, useful data, the value your team delivers will be clearer and more tangible to them. Success in analytics is about driving business outcomes—what you accomplish with the data—not the quantity or wizardry of the data products you produce. As a final point, these two approaches will use AI very differently. The 'pass along' approach will use it to shovel more data at a faster pace, piling on to the information that is already being ignored. The other will use AI to simplify the data complexity and help more business users extract better insights, which will expand user adoption. Do you agree with my take? What approach is your analytics team using?

AI Agentic-based data analytics will completely overturn how data analytics is done traditionally, why? 1) LLM-based tools are not deterministic, they are ML systems overall that need tuning 2) LLM tools lack the domain-specific context needed to tailor insights to business needs. They surface insights quickly but the insights are not right. 3) Analysts, in turn, must spend significant time drilling into results and explaining where and how the answer is coming from. You have to enable your people before trusting the AI. Here is my advice when integrating AI into your analytics workflow: 1) Tools should ensure data analysts can use them comfortably, not replace them. 2) Prioritize highly valuable but repetitive, objective, and validatable use cases. Identify deterministic, statistical techniques to catch AI mistakes. 3) Focus on interpretability first but automation second. Your data analytics must be explainable before using it for decision-making 4) Build feedback loops to improve prompts and accuracy over time.