At any networking event there are people that you want to avoid because they cling to you, and prevent you from meeting new people. Usually an effective technique is excusing oneself to refill your drink or using the restroom. However, recently I was resought out after politely excusing myself. “What was i to do now?” I had gone to the restroom and I had a full plate of food. The solution, I handed them my business cards and told them to call me.
This was the perfect solution. By handing them my business card I was able to walk away and continue to network with other people. Most likely the person will never call me, because 99% of the time, people NEVER CALL. If he does call then he will probably be a lead and not a clinger. While I needed to continue to network, I can take the time in a one-to-one meeting if he is interested enough to continue the call. (more…)
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Why Business Cards Don't Work
Presenting Predictive Analytics
The nature of forecasting the future makes presenting predictive analytics unique and challenging. There is no flashy server or dashboard that will make presenting analytics any easier. There is only a model that tells a story about the future of users’ business, customers, non-customers and competitors. While models are very valuable they are not your typical business intelligence artifacts. To produce a meaningful return on investment you need to translate the details of the story into results that can be applied to a specific business question.
While you can not replace sound scientific and statistical methodology, CAN has found that users don’t care about a model’s Durbin-Watson, standard error, or R², or they are not familiar enough to properly understand the statistical nuances. The key to proving that a model works and getting political support required for implementation is to ask the experts if the story the model tells reflects reality. It is also valuable to prove that a model works by letting the prove itself over time.
It is important to note that predictive models typically do not provide solutions to business questions, but instead often offer incomplete answers and important insights. When presenting predictive analytics your audiences expectations should be set on becoming less wrong, instead of finding the perfect solution. CAN finds that users appreciate our philosophy of Less Wrong. While it seems counter intuitive, our lack of hubris builds confidence in our models and sets realistic expectations. The basic principle behind, Less Wrong is that in business winners are not right, they are simply less wrong. There are no perfect answers in complex sciences, such as data science and predictive analytics, only less wrong answers. The goal is to reduce the uncertainty of making the wrong decisions, not thinking uncertainty can be eliminated.
In conclusion when presenting predictive analytics don’t be afraid to kill your darlings. If you can not justify an element of your presentation get rid of it. This will help you focus your presentation, your audience will listen and the results of your hard work building predictive model will get implemented.
Applications of Predictive Analytics
The following are some examples of how predictive analytics can be applied in financial services, retail and manufacturing. This list is not comprehensive, but it provides some interesting applications.
In the financial services the cost of making the right decisions provides marginal benefits, while making the wrong decisions can have significant costs. Most applications of predictive analytics in the financial services industry help companies avoid making the wrong decisions. For example, credit card companies are able to determine who is most likely to default on their credit cards in the next 6 months by applying predictive analytics to customers purchases and demographics.
In retail understanding customers is essential to success. Retailers have provided customized shopping experiences by using predictive analytics to understand the drivers of profitability, loyalty and activity for each customer segment and develop specific campaigns for each segment. This has allowed retailers to wow customers with personalized services while scaling and keep prices competitive. Predictive analytics have helped both offline and online retailers determine which products to carry, optimize marketing plans, and develop promotional and loyalty programs. Imagine only offering effective loyalty promotions to profitable customers at risk of leaving, while avoiding offering discounts to unprofitable or already loyal customers. Another example is knowing what a customer is most likely to purchase next, so that your staff or website can make informed recommendations.
Manufacturing is about knowing what, how, and how much to produce. Predictive analytics have helped manufactures manage their supply chain and production schedules by accurately forecasting demand, and have helped manufactures produce goods in the most effective way possible by predicting failure of equipment, monitoring workers, and identifying ways to eliminate inefficiencies. For example, CAN has helped companies with tens of thousands of sales each month forecast sales within a 10 to 50 units, so that they can optimize production schedules and supply chains. Also, imagine being able to understand why different managers have different levels of employee turnover, employee injuries, and equipment failure.
Beyond specific applications, predictive analytics has the unique ability to help companies become less wrong, scale decisions and systematized learning.
Less Wrong: The basic idea of Less Wrong is that in business, and almost anything in life, you can never be perfectly right, but you can be less wrong and by striving to continually become less wrong you get closer and closer to being right. By using predictive analytics you will not get the perfect answer, but you can determine what is happening, what most likely happen and most is most likely the right thing to do. For example it might be really nice to know exactly what commodities prices will be in a month, unfortunately this is not possible. However, using predictive analytics you might be able to predictive with 70% accuracy which direction a commodity price will trend and if this is better than before predictive analytics then it most likely will be worth the investment. Another example would be picking which commodity would most likely be the best investment in the next 6 months. The reason that predictive analytics can’t produce exact results is because it is not a simple science, for more read my post on Simple vs. Complex Science.
Scale Decisions: Predictive analytics has the unique potential to allow executives to scale their decision making as organizations and decisions become increasingly more complex with ever thinner margins for error. Predictive analytics can be used to create a model of the business based on the organization’s data and executives’ theories. For example an experienced sales manager will be able to determine which sales leads will most likely respond, purchase and be profitable customers, however he or she does not have time to review every lead for a 200 person sales team. Also, while a sales manager knows what make a lead worth pursuing, he or she most likely finds it difficult to communicate the rules and criteria to their team. Using predictive analytics a sales manager can develop a model to score incoming sales leads. This model can be coded directly into the company’s customer relationship management (CRM) system so that leads are scored as soon as they are entered into the system.
Systematized Learning: In the future, profits will be directly related to your company’s rate of metabolizing new knowledge, as opposed to renting out existing knowledge. Predictive analytics can increase your company’s ability to metabolize new knowledge by continually studying the data produced by your company, customers, non-customers and competitors to find important patterns that will impact your business. For example, predictive analytics can help executives identify when customers stop responding to a certain campaign and why.
While every company can benefit from becoming analytical, like any other tool, predictive analytics can not fix anything. However, it is most certainly the next step in the evolution of business intelligence. If properly applied, predictive analytics has the potential to help businesses work smart. Read a related post on when to and not to apply predictive analytics.
Contemporary Analysis: Testimonial from Universal Information Services
Universal Information Services enlisted the services of CAN to improve the measurable impact of our Google Adwords campaign and drive more relevant prospects to our website. Their approach seemed to accomplish our goal, but used a methodology that made the process both cost effective and easy for us to understand from a non-technical perspective.
Ultimately, nearly every measurable metric from Google indicated a sizeable increase. Most importantly to us was that our ad placement greatly improved from either not showing at all or being near the bottom, to ranking between 3rd and 5th in nearly every ad we had running.
Contemporary Analysis used an approach where they gathered baseline data on what we had been doing on our own, then suggested and implemented some changes. This initial change was tracked for 30 days, evaluated, and then modified again to further extend our benefits. We underwent three rounds of this track, modify, and measure process. Our final report proves out that the Contemporary Analysis model worked for improving our Adwords performance and has increased the number of relevant leads we receive from our website.
I am confident we will again use Contemporary Analysis to continue pushing the success of our Google Adwords campaign as well as optimize our website to reinforce this campaign.
When to Apply Predictive Analytics
We love predictive analytics and if we are not careful, it is easy to start reducing everything into predictive models. I have even caught Tadd standing at the window collecting primary data on the smoking habits of the people in our building. To make sure that CAN and predictive analytics experience continued success, we have developed a guide for when to apply predictive analytics.
First, predictive analytics should not be applied if:
The cost of being wrong is low.
You should not apply predictive analytics if reducing uncertainty does not provide enough value. Predictive models should only be applied in situations with a high cost and/or probability of being wrong and where predictive analytics can provide information to reduce uncertainty. To determine if predictive analytics is worth applying to a decision you need to calculate the expected value of information. In the book How to Measure Anything, Hubbard provides the following formula, expected value of information is equal to the difference between the expected opportunity loss before and after information. The expected opportunity loss is equal to the chance of being wrong multiplied by the cost of being wrong. (more…)
How to Get Referrals from Clients
Referrals from current clients are completely different from referrals from referral partners. Getting referrals from your current clients require a different conversation, because they already know you, your product, and the benefits. One of the best I know at having the conversation is a friend of mine named Amy. She has developed a system that allows her to receive 10+ referrals per week from her current clients.
I asked her how she was getting so many referrals, when most people are happy with one or two per week. She told me not to be satisfied with just one or two referrals. “I used to get only one or two per week until I started expecting three or four from each person. Unless you make it a point, they don’t know how many they are supposed to give you. After setting expectations clients usually just keep providing names of people that I can help, until I stop them.” (more…)
Dashboard Design: Design for Parallel Processing
The value of dashboards and visualizations are that they allow users to shift from serial to parallel processing. When reading a block of text you can only process the information serially by starting at the top left of the text and finishing the bottom right. Dashboards and data visualizations allow you to absorb information in parallel making it easier to absorb information quickly, identify relationships and trends.
Download our eBook, “Dashboards: Take a closer look at your data”.
However, the lack of serial processing requires that dashboards be effectively designed so that information can be absorbed as easily as possible. This requires that dashboard be designed for pre-attentive processing or for “the unconscious accumulation of information from the environment” (Wikipedia). Pre-attentive processing is specifically designed for parallel processing. Pre-attentive processing allowed our ancestors to continually scan the horizon to identify opportunities and threats. If well designed, a dashboard is modern-day equivalent of the horizon of the savanna, a data rich experience where it is easy to absorb the most important information, identify relationships and spot trends.
The basic principle of designing a pre-attentive dashboard that enables parallel processing is to keep element natural. Replace bright bold colors with neutral and natural hues, and pie charts, gauges and traffic lights with hue, intensity, location, orientation, line length, line width, size, shape, added marks, enclosure, and motion.
Three Types of Dashboards
A dashboard is a single display that in a glance provides essential information for a specific objective. Since you are limited to a single display capable of being monitored at a glance, the first step of dashboard design is to select the purpose of your dashboard. This provides you with a filter to make sure that your dashboard effectively accomplishes its intended purpose.
Will it be strategic, analytical or operational? Answering this question will keep your dashboard from falling victim to trying to be everything to everyone.
Strategic dashboards provide managers and executives at all levels of the organization the information they need understand the health of the organization and help identify potential opportunities for expansion and improvement. Strategic dashboards do not provide all the detailed information needed to make complex decisions, but instead help executives identify opportunities for further analysis. A strategic dashboard should be simple and contain aggregate metrics the represent the over all health of the organization. Typically there is no need for interactive features and the data should be updated no more than monthly.
Analytical dashboards provide users with the data they need to understand trends and why certain things are happening by making comparisons across time and multiple variables. Analytical dashboards often contain more information per square inch than both strategic and operational dashboards. Since understanding is the goal analytical dashboards can be more complex than strategic or operations dashboards. Also, while analytical dashboards should facilitate interactions with the data, including viewing the data in increasing detail, it is important to maintain the ability to compare data across time and multiple variables. If you lose the ability to compare data then an analytical dashboards is no longer able to accomplish the goal of allowing users to understand trends and why things are happening.
Operational dashboards are used to monitor real time operations and alert the users to deviations for the norm. This often means that operational dashboards need to be updated frequently if not in real time, contain less information than analytical or strategic dashboards, and make it nearly impossible to avoid or misunderstand an alert when something deviates from the acceptable standards. Operational dashboards should provide users with specific alerts and provide them with exactly what information they need to quickly get operations back to normal.
Download our eBook to find out more about using dashboards to get a better look at your data:
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Gain Support for Predictive Analytics
The decisions you make in business may never be perfectly right, but, you can strive to become less wrong. Predictive analytics provides decision makers with a system to continually improve decision-making, while eliminating some of the inefficiencies of non-analytical trial and error.
However, the ability of predictive analytics to systemize an organization’s cumulative knowledge can be threatening to experts who value their accumulated knowledge over continual learning. When beginning a predictive analytics initiative, the most vital key for gaining political support is to maintain focus on the business problem, and never the technology. By focusing on the specific problem, you will deploy predictive analytics only if it is the right tool for the job. This ensures that the initiative has a greater likelihood of success, has the support of key internal stakeholders, and because predictive capabilities are leveled at a specific target the initiative gains executive buy-in. “A business problem exists, and this is how we are going to solve it.”
While predictive analytics may be ‘the best man for the job’, expect there to be resistance to implementation. Often, this comes from individuals who rely on their accumulated knowledge as a competitive advantage against their team-mates. In light of this situation, quite possibly the most effective method of gaining support, is to focus predictive analytics on a specific, defined business problem. Your initiative must be dialed in on solving vital, business critical issues. This way, dissenters to implementation will be seen in the light of hindering the future success of the organization.
Consider the following case study. One of our clients had a division that is responsible for sourcing materials for production. They had a group of commodity traders that were responsible for sourcing materials at the best price possible. While their expert traders had a great track record of forecasting the market, their best traders were nearing retirement. Also, while the company had made significant investments into business intelligence, the amount of data required to make an informed trade had been growing exponentially for the last ten years. The future of the organization required developing a system that made learning from the cumulative knowledge of the organization easier. However, trying to come in with predictive analytics was politically challenging, as it could be threat to both business intelligence and the company’s best commodity traders. To overcome that perceived threat, we had to focus on the business problem and make a case that eliminating the problem was essential to the continued success of the organization.
Our client had to systematize the knowledge of those traders nearing retirement, and also develop a solution to find valuable patterns in the increasing flow of data. The solution that we developed was simple. We built a model that forecasted the direction of commodity prices. Before our model, traders and business intelligence had been spending a significant amount of time determining the direction of the market. Now, by leveraging predictive analytics, traders and business intelligence have moved up the value chain. Instead of spending the majority of their time trying to determine the direction of the markets, they spend the majority of their time quantifying the direction of the change. This resulted in the improved performance and value of both business intelligence and the commodity traders.
In conclusion, the key to gaining political support is to define the business problem in the context of its importance to the continued success of the organization. If solving the business problem is not essential to the success of the organization, it may not be worth addressing. If it truly is important, and predictive analytics is the best candidate for the job; internal opposition will be seen as coming from selfish protectionists who threaten the continued success of the organization.
How to Convert Leads into Referrals
In “Lead vs Referrals” I talked about the difference between a lead and a referral and why referrals are superior to leads, but the question arises, “How do you get people to refer you instead of giving you leads?” The answer is purposeful and tactful coaching. The best people at getting referrals do not get them by accident. They ask and coach.
The first step is networking. You must have a business network that is actively looking for leads for you. They must be the types of colleagues that are in the right place. After all, sales is just two things: Being around the people who want or need to buy your product, or being around the people who are around the people who have the want or need to buy your product. Many people try to do this at the networking event. It usually goes something like this: “Hi, how are you?. Will you please refer me because I do x,y, and z and it’s fantastic.” This is technically selling which is one of the big no-nos of networking. See point two in “Why Networking is important and Tips for Success.” (more…)
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