Use Sales Data to Develop a Customer-Centric Sales Approach
•Download as PPTX, PDF•
0 likes•4,094 views
This document discusses how companies can leverage sales analytics to improve business performance. It recommends analyzing five key areas: sales pipeline accuracy, sales performance, customer profiles, customer behavior, and sales targeting. Advanced analytical techniques like machine learning can provide deeper insights into customer motivations and traits to better inform sales strategies. Maintaining a data-driven approach that prioritizes ethics can help companies enhance customer segmentation and identify the best prospects to target.
Recommended
More Related Content
What's hot
What's hot (20)
Similar to Use Sales Data to Develop a Customer-Centric Sales Approach
Similar to Use Sales Data to Develop a Customer-Centric Sales Approach (20)
More from Analytics8
More from Analytics8 (7)
Recently uploaded
Recently uploaded (20)
Use Sales Data to Develop a Customer-Centric Sales Approach
- 2. Unleash the power of your data Analytics8 is a data and analytics consultancy. We help companies make smart, data-driven decisions by translating their data into meaningful and actionable information. For us, data is not just data. It's an opportunity to innovate, support, and transform. We know data is power and with it, we will help you unleash yours.
- 3. UNDERSTANDING THE CUSTOMER SETS THE TONE FOR YOUR SELLING STRATEGY
- 4. USING DATA TO ADAPT SALES STRATEGIES
- 5. IMPORTANCE OF SALES ANALYTICS
- 6. THE IMPORTANCE OF SALES ANALYTICS See more than how you did; understand what to do Better understand customer behavior to inform all aspects of your sales strategy It's necessary to truly scale and improve your sales organization
- 8. P I P E L I N E A C C U R A C Y S A L E S P E R F O R M A N C E C U S T O M E R P R O F I L E S C U S T O M E R B E H A V I O R S A L E S T A R G E T I N G THE FIVE AREAS TO ANALYZE
- 10. SIMILAR CUSTOMERS STILL HAVE IMPORTANT DIFFERENCES
- 11. CUSTOMER PROFILES The more data points the better company buying habits, industry info, employee info, company revenue, etc.
- 12. SALES DASHBOARD: WHAT ARE CUSTOMERS BUYING? Company x Company y Company z $$ $$ $$ $$ $$ $$ $$ $$ $$
- 14. INTRODUCE ADVANCED ANALYTICS TO GET MORE ACTIONABLE INSIGHT • Move beyond what and get to the why and how • Provide inputs about customer traits, product traits, usage patterns, etc • Programmatically analyze the data to uncover importance, anomalies, changes in habits and behaviors • Cyclical nature enables improved models based on new data
- 15. VALIDATE MODEL ON NEW DATA CUSTOMER TRAITS FEATURES USAGE PATTERNS PRODUCT TRAITS TEST MODEL SCORE MODEL IMPLEMENT MODEL TRAIN MODEL CHURN NO CHURN MACHINE LEARNING MODELS Continuously fine tune your model to make it more effective
- 16. CHOOSE PROJECTS THAT BRING VALUE Ethical Data Science
- 18. SALES FORECASTING NEEDS TO BE ADJUSTED What will my sales be this month, next month, next quarter, etc? • COVID has thrown a curveball in sales forecasting • Can't assume that once predictable business operations will behave the same
- 19. SALES DASHBOARD: ACCOUNT HISTORY + PAID ON TIME Company A Company B Company C Company D Company E Company F Company G Company H Company I Company J Company K Company L Company M Company N Company O Company P Company Q Company R
- 20. SALES DASHBOARD: OPPORTUNITY DETAILS Company A Company B Company C Company D Company E Company F Company G $$ $$ $$ $$ $$ $$ $$ $$ $$ $$ $$ $$ $$ $$ $$ $$ $$ $$ $$ $$ $$ $$ $$ $$ Qlik Sense Development Migration to Qlik Supply Chain analytics dashboard Cloud Migration to AWS Geospatial Analytics Qlik and Power BI Training Mapping Services
- 22. SALES PERFORMANCE Sales Reporting: Who is selling more or less? Is remote selling working? How are sales reps doing? Sales Analytics: What techniques are producing sales?
- 23. SALES DASHBOARD: SALES REP PERFORMANCE $$ $$ $$ $$ $$ $$ $$ $$ $$ $$ $$ xx xx xx xxxxxx $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $$ $ $ $ $ $ $ $ $ $
- 24. SALES DASHBOARD: PERFORMANCE SCATTER PLOT Johnny A Bilbo B Tyrian L Tom H Dwayne J Charly T William W Trey Bayne Michael J
- 25. SALES TARGETING
- 26. SALES TARGETING Enhanced customer segmentation helps identify the right messaging for the right customer at the right time Who should Sales target? Segment/Cluster Groups • Demographics • Geographics • Product channels • Customer lifecycle (to name a few) Apply Machine Learning Techniques to Identify Significant Drivers • Supervised (has targets) • Unsupervised (i.e. Clustering) Hone Marketing Efforts – Drive Product/Service Innovation
- 27. BEST PRACTICES • Have clear direction • Enhance customer data collection to improve targeting • Start with a solid data strategy • Leverage existing analytics and BI dashboards • Ethical data science MACHINE LEARNING IS MORE THAN THE MODEL Best Practices
- 28. ML MODELS UNCOVER CUSTOMER MOTIVATIONS Which customers are the appropriate target for a product/service? Interpretable ML models help us understand customer influences and behaviors, helping to increase sales conversions Customer profiling tells us who shares similar traits. But why? Profile customers Apply ML model . . . .. .. . . .. . .. . . . . . . . .. ... . .. . . . .. .. . . .. . . . .. .. . .. . . .. . .. .. . .. . .. . . . . . . . ...... .. . . . ... . ... ..... .. . ... ..... .. . ..... .. .. .. . ... .. .. . . . . . . . . .. . .. . . . .. .. .. . . . . .. . . .. . .. .. . .. . .. .
- 29. SUMMARY: HOW TO GET STRONG SALES ANALYTICS How much of this are you already doing? P I P E L I N E A C C U R A C Y S A L E S P E R F O R M A N C E C U S T O M E R P R O F I L E S C U S T O M E R B E H A V I O R S A L E S T A R G E T I N G
- 30. QUESTIONS? MATT LEVY mlevy@analytics8.com TREY BAYNE tbayne@analytics8.com SUBSCRIBE TO THE 8 UPDATE NEWSLETTER • Analytics8.com
- 31. We know data is power and with it, we will help you unleash yours. #salesanalytics
Editor's Notes
- When I was a kid and first learning to drive my mom would give me $10 and tell me to go to the grocery store to buy a loaf of bread. Though the grocery store is not an exciting place to go for a a 16 year old, it gave me reprieve from my irritating siblings and she always let me keep the change. Since I always got to keep the change I constantly looked for ways to save a little bit of money on the bread. I’d hunt for coupons and shop the sales. I hit gold when I found the grocery discount card. You’ve seen these cards. You scan them at the grocery line and get to take advantage of member only discounts. Those little cards give you big savings -- and you give the grocery store your sales data. Think for a moment about the type of information they can glean from your discount cards. They know what kinds of items you like. They know what stores you visit. They know when you like to buy groceries and what groceries you prefer in any given season. They also know how often you shop for certain items, allowing them to stay stocked without letting food rot. Understanding their customer sets the tone for their selling strategy.
- When COVID-19 hit, people’s buying habits changed. More people chose to eat at home instead of brave the restraunts. At the same time, going to the grocery store raised concern of exposure. This resulted in the need for larger grocery runs less often and a quick shift to online ordering. Buying habits changed overnight and grocery stores had to shift selling strategies quickly to stay afloat. Grocery stores paid attention to the customer buying habits during COVID 19 and when toilet paper vanished from US shelves, they did more than just try to restock toilet paper. Instead, they paid attention to how customers were buying and why their buying habits were changing. It’s not that more people came in and bought more items. It’s that fewer people were making fewer trips and buying in bulk. To meet customer demand they began to promote purchasing online where they could ship items directly from the warehouse, control stock, and calm
- ----------------------------------------------- TB >> Updated Talk Track 11.11.20 Too often the metric for success in sales comes back to whether you are actually… well… selling. If the grocery stores had ony paid attention to the decrease in stock in toilet paper they would have bought more, stocked their shelves, and waited for customers that would have never come. Keeping track of what you sold only tells you that what you were doing worked when you did it. It does not tell you if you have a successful sales strategy. What if, prior to covid you had been spending a bit more time trying to understand not if a person bought, but why a person would. What if you better understood each customer’s habits better? If they responded better to calls in the morning vs the afternoon would you change your approach? If you understood if they preferred emails or calls would you change the way you contact them? If you could easily watch the history of a clients interactions and what was happening before each purchase, would it change your approach during a time when no one was purchasing? My guess is it would. The problem is that getting that level of data is difficult in the best of times. It depends on proper capture mechanisms and ways of gathering that data from multiple sources. At Analytics8 we use Sales Force and Marketo to help manage our incoming pipeline. For years we thought we had it all figured out. We had a little screen from Marketo embedded in SalesForce that would tell us if a customer responded to one of our marketing initiatives. That worked great for understanding when a customer got something from us (downloaded a pamphlet, signed up for a webinar, etc. ) but was clunkcy when trying to determine if a customer got everything they needed from that single touch or if they wanted to talk to us more. We could go to a separate screen and see if the client ever purchased from us (their history) and then another screen to see how many times we reached out before it happened (our effort) and then another screen to find out who we last talked to (our relationships) and then LinkedIn (additional contexteual information). That’s a lot of work just to find out the likely true interest of a customer.
- To help, we put a standard BI tool on top of SalesForce and Marketo. It allowed us to see information about a customer across many different Salesforce reports. Not only that, it allowed us easily see our customers behaviors, the opportunities currently in our pipeline, the proven best practices of our senior sales staff. Essentially, it gave us a better view at what was, and was not working, in our sales organization. So, when COVID happened, we could easily see what was causing the dip in Sales, what was working despite the dip, and scale that practice. For those of you who follow us you saw an implantation of our findings last quarter when we leaned in heavily to Recovery Analytics. We saw our customers responding well when we reached out to help in their recovery by leveraging data. That campaign was a direct result of feedback we got from Sales Analytics. We are not done, we anticipate leveraging machine learning in the near future to better understand customer behavior in ways we can’t. It’ll be great to have this same conversation with some of you next year.
- ----------------------------------------------- TB >> Updated Talk Track 12.2.2020 This is the opening page for the dashboard I was talking about. We’ll see a number of screen shots inside this application throughout this webinar but this is the view before we start drilling in to the data. You can see here how we have our reporting broken down into a number of different pages. Each page shows us a different angle of how the customer is responding to us and how we are responding to the customer.
- ----------------------------------------------- TB >> Updated Talk Track 11.11.20 So what do we need to Analyze. We already said that looking at how much we sold is not enough. It’s also true that looking at any one of the items listed here by themselves provides a skewed view of your sales organizations. After all, understanding how your Customer behaves at the end of a quarter can massively change how that customer fits in your pipeline accuracy and forecasting abilities. At Analytics8 we think a successful sales strategy leverages data from 5 key topics Customer Profiles Customer Behavior Pipeline Accuracy Sales Performance Sales Targeting During this webinar we’re going to look at each of these independently and discuss the value we can gain each. ----------------------------------------------
- Customers often share similar characteristics but have small nuances. For example, I spent many years before the consulting world working with a supplemental insurance company. We’ll call them Duck insurance company from now on so I can reference them later. Duck Insurance never started projects in the 4th quarter. 4th quarter was crazy for them trying to get polices out the door and claims paid before the new year. Turns out, this is true of many insurance companies. It’s just how the industry works. Duck insurance was also great at keeping people around for a long time. It was not abnormal to have 4 people with over 40 years of time at the company at any given moment. Turns out, this was a nuance of Duck Insurance and not an industry standard. Both facts about the company are important. One is an industry standard and the other is a specific nuance of that client. Knowing 4th quarter is bad means I don’t try to sell things to them then. Knowing they keep people around let’s me know there is a lot of stored information in a few people and spending time with those people will get me places much faster.
- ----------------------------------------------- TB >> Updated Talk Track 11.11.20 Some information, such as those I just talked about, come from an intimate knowledge of the customer. This type of information is valuable can be captured in a tool like SFDC and shared with the larger group in the form of say, a scatter chart where the purchase volume or frequency can be set over different timeframes such as quarters or year. What happens if you don’t know the customer and are simply trying to find new customers. How do you profile a customer you don’t know? You likely drive past an industry that does this really well and never thought about it. We have a road near where I live that has 5 car dealerships on it. They all sell a different kind of car and they are all competitors. It turns out that if a customer is looking for a car they are likely to visit a number of dealerships. If the dealerships are near each other they have an opportunity to pull from the car buying customer pool that each dealership pulls to the area. Car dealerships have profiled their customer. See, if a car dealership was near a place that sold shoes it wouldn’t make much sense. The budget of a person looking to buy shoes is very different from the budget of a person trying to buy a car. If you aren’t looking at data that tells you the company industry, the products they use, the revenue of a customer, how much they should could spend on your product, when they normally buy, etc, you may be wasting your time trying to sell a car to a person looking for shoes. >>> Need to roll in to [SALES DASHBOARD] slide 8 better. Needs a bit more explanation. ----------------------------------------------- TD >>> I like the story you tell here, but I think you need to relate it to how someone would use data to build a customer profile. You knew this about Duck Insurance because you worked there- but how would someone who didn’t have this inside knowledge of a company build a customer profile? I think you just need to add some content around this- but keep this story. Might need another slide or two- explain how/ where you can use your data to build profiles about companies. JM>> could probably accomplish this by discussing the importance of adding contextual data to general customer profile data: when you add contextual details (employee demographics, the technology they use, etc) to obvious external factors that describe your customer profile (like industry data), you discover the value can you provide
- -------------------- TB >> Updated Talk Track 12.3.2020 At Analytics8, our version of trying to sell a car to someone shopping for shoes would be trying to sell a software package or consulting engagement to someone heavily invested in something different. Software and the consulting services to scale out a software can be a large investment and switching can be costly. We use our dashboarding tool keep track of what kinds of tools and services a company has already invested in and how often so that we don’t offer something that is completely outside of their wheelhouse. This is a screenshot of one of the sheets in our internal sales dashboard. It tells when customers have spent money, what they have spent money on and how lucrative that customer is. This particular customer uses a tool called Qlik and has engaged us to help them build with it recently. If I went to them and tried to pitch Tableau, another BI tool, it would likely fall flat. The customer profile tells me what solutions they are warm to and which they are not; saving me time and social capital with the customer
- Matt
- MATT Intro- explain if you want to really understand customer behavior- you need to go beyond traditional analytics and use some more advanced techniques. explain high level what you mean by data science/ ML models. When it comes to analyzing customer behavior- very important to understand what customers are going to churn or not. You want to be able to keep your customers no matter what phase they are in. How to do that- look at traits like customer traits, product traits, usuage patterns and put that into your model. These are inputs into your model. You need to be able to find the best model and then validate it. These models can be further fine tuned to do customer segmentation- when you break up customers by segments- you will get better resutls and can address better targets instead of having a catch all category for all topiocs/ needs/ etc Customer profiles are input into the customer behavior.... How to assess churn. During this process, can ID different segments. TD NOTES >>> I am guessing that once we know more about the talk track, we will probably need to add some slides. I think there is a lot of info to unpack around this topic. ORIGINAL NOTES >>>> How have my customers buying habits changed? What products/ services do they want? How do I put my customers into groups so I can better target my messaging and sales efforts?
- Trey
- ----------------------------------------------- TB >> Updated Talk Track 11.18.20 Now that we understand what our customer looks like and what causes their buying habits to change, we can get a better handle on Pipeline Accuracy and Sales Forecasting. If we go back to the grocery store analogy I used at the beginning we discussed the epic toilet paper shortage of COVID 2020. Grocery stores understood that buying habits had changed but not necessarily usage. Just because more toilet paper was being purchased, it didn’t mean we had found alternative uses for it that would cause demand to continue to increase. Knowing this allowed grocery stores to properly forecast based on customer needs and buying habits and not whether their items had been selling. If instead, they had assumed that sales begot sales that begot sales, their forcast would have been grossly overestimated. Too often we see people looking at their sales from last year, checking their gut, and assuming that business will continue to grow because their pipeline looks full. In many cases, that would be true. But when the buying habits of billions of people change, growth is far from certain. Instead, they should have used data to look at the reasons customers were buying. When those reasons change, their buying habits change and impact Pipeline accuracy.
- This is another sheet I our sales dashboard. This sheet is often used to look at the history of an account. I’ve made some selections to filter to accounts that we’ve done business with in the last 6 months that have been invoiced and which they have paid those invoiced on time. It’s not that I don’t have additional accounts I’ve worked with, they just might not have done work recently or they haven’t paid invoices on time which is not a surprise in the economic situation we are in. These customers on the other hand are doing work. That tells me they have a budget, a need, authority and a timeline; all powerful indicators of continued future work.
- Switching to another sheet in the sales dashboard I ‘ve carried the same accounts over to see what open opportunities we have with those clients. Again, this sheet does not tell me all of my open opportunities, but it does show me the pipeline I have with customers I have a high confidence in. Based on the data, These customers are very likely to result in closed deals and generate income before the end of the year. By looking at our most resilient customers we’ve overcome the inherint fragility of pipeline accuracy. Now our sales forcasting is built on data driven experience instead of hopeful thinking.
- ----------------------------------------------- TB >> Updated Talk Track 11.18.20 Finally we get to Sales Performance. This is what many people thing about when they think Sales reporting. Who’s selling more? Which location or salesperson is making their targets? How many calls are they making and emails are they sending? Take note that at this point we’ve switched focuse. In all previous sections we’ve talked about the customer but here, we talk about what the seller, or product, is doing. I have kids and we often drag them to Target. If you don’t have kids, you should know that most kids hate target and see it as a place of torture. That is, until you get to the Toy section. In my Target, the shelves in the toy section are set lower than elsewhere in the store. Just low enough that my kids can see all the things they absolutely *NEED*. If you take them to the bedding section, the shelves are placed higher, where they can’t see them. ( come to think about it, just high enough for my wife to see the things she *NEEDS*.) Target is doing this because they know their target audience, but they are also measuring their assumptions to see if they work. When they aren’t working, the item on the shelves move or get that glorious red sales sticker. See, the data question here is not how much are you selling, but what techniques are producing sales. In our world we keep track of calls, emails, marketing campaigns, and the like and compare that to what turns in to an engagement. It’s a way for us to validate our techniques and hone what works
- This is another page on our sales dashboard. I know it’s a bit difficult to see but the table at top shows the salesperson on the far left and different ways of measuring the dollars brought in. The tables at the bottom give us a view of the activities.
- Another view shows us a scatter plot that compares amount sold to profit gained. This allows us to easily see what products are being sold by who that gain us the most profit.
- Matt
- MATT Will add process flow on how to build a clustering model. Who should the sales people target? How can sales people improve their chances converting a prospect? Customer segmentation Different analysis/model for each segment Further segment by other demographics (region, age range, product channel, etc.) Identify needs of specific customers Targeted marketing (e.g. A/B testing) by life-cycle stage and/or demographics Better innovation through understanding targeted needs not just general needs or follow-on products Voice of the customer translates to inputs for machine learning models Data collection, organization and data profiling essential to an effective model and adoption of any machine learning integration Incorporate results into BI dashboards salespeople already using, or even give them ability to run models based on new inputs on the fly
- MATT
- What drives your customers? Segment them into groups based on drivers. That only gets you segments- need to understand what those segments are- so you can actually take that to use for marketing etc…
- TB >> Updated Talk Track 12.3.2020 Thanks Matt, we’ve made it to the end. To recap we talked about 5 different areas to analyze to improve your overall sales strategy, most focused on the customer, and very little on the number of widgets you’ve sold. My question to you is, how much of this are you already doing? Next, of the items you are doing, what are you doing well? There are organizations that have most of this figured out but there are a lot more don’t. If you’re one of those that don’t I suggest you pick one area to start instead of trying to do them all. I think the easiest is Customer Profile. Most sales organizations have some sort of CRM tool like Salesforce to manage their customer database. Look in the CRM and see if you track activity there. Do you have the companies net revenue saved at an account level? What about their industry? Can you get information about what types of products they use by doing a job search on a website like Monster to see what roles they are hiring for? Most of this information is freely available and just requires updating the processes used to get the information documented. All of this information adds to the profile of the customer that helps you understand the who’s, what’s and when’s of a customer. From that point you just need a good way to report on that data. Let me share with you though some things we learned from experience. Your CRM will only get you so far. It’s designed to capture information and telling you what was captured is an afterthought. It’s no surprise Salesforce bought Tableau last year. Salesforce stores data, Tableau visualizaes it. There are a lot of resources on the web to help build out a Sales Analytics Reporting platform; many of which are at Analytics8.com. However, if you need a hand we’re here to help. After all, we’re doing it now. >>> Your CRM will only get you so far.
- Matt and I have spent a lot of time talking and now would be a good time to hear from all of you. Continue asking questions in the questions box. Matt, have you been looking through those? Any questions you want to jump on?