Показаны сообщения с ярлыком predictive analytics. Показать все сообщения
Показаны сообщения с ярлыком predictive analytics. Показать все сообщения

вторник, 1 апреля 2025 г.

How to predict your competitor’s next move

 


By 

Know which competitors matter the most and predict their next steps with greater accuracy.

In this episode of the Inside the Strategy Room, John Horn, author of the new book Inside the Competitor’s Mindset (The MIT Press, April 2023), explains how to predict competitor actions. Horn is a professor at Washington University’s Olin Business School in St. Louis who helps companies maximize the value of competitive insights. He spoke with Emma Gibbs, who leads McKinsey’s Strategy and Corporate Finance Practice in the United Kingdom, Ireland, and Israel. This is an edited transcript of the discussion. For more conversations on the strategy issues that matter, follow the series on your preferred podcast platform.

Emma Gibbs: What is the core message of your book?


John Horn: The big idea is that many companies do competitive intelligence, but where they fall down is in turning that intelligence and data into insights about what the competitor will do. Many of my clients say that their competitors are irrational, but this is because they are not taking the time to look at the world from their competitors’ point of view. Once they do, those competitive actions start to make sense. The book sets up frameworks and processes to help executives get inside the competitor’s mindset.


Emma Gibbs: Why do you think companies have such a hard time understanding their competitors’ points of view?


John Horn: One reason is that we assume our approach and the way we look at the world is right. When someone does something differently, it creates a dissonance with what we think is the correct answer. The other reason is that with more seniority, power, or status comes greater difficulty in being empathetic. We made choices that got us promoted, so we assume they had to be right choices. Any others don’t make sense to us.


Emma Gibbs: Have you come across instances when a company does act irrationally?


John Horn: I have yet to see a company act truly irrationally. Typically, the competitor’s actions aren’t irrational but are moves that we wouldn’t make or we don’t want them to make. I did a war game with a transportation client, and we discussed whether we should include warehousing space as a choice in the game. The client said, “No, because our competitor is irrational with their warehousing. It’s a mature industry with excess capacity and they are adding warehouse space.” I asked, “Have you added capacity in the past 18 months?” He recited a short list: expanding one facility, adding another, acquiring a third. I said, “So you’ve added or expanded 12 facilities. Your competitor added four, and they’re irrational?” He looked at me and said, “Yeah, but there is a good reason why we added those 12.”


Emma Gibbs: How would you approach figuring out the competitor’s reasons for their moves?


John Horn: It’s about breaking out of your own mindset and forcing yourself to look at the world from competitors’ points of view. If I had their assets, what would I do with them? If I decrease my prices and the competitor matches that decrease, I won’t gain any market share. Any time you plan to build a new plant or acquire a company or change your pricing, you should think, as in any game, “If I make this move, what will my opponent do in response?” The minute you say, “They’re irrational,” you stop trying to understand them.


Emma Gibbs: In your book, you have a four-stage framework for understanding competitors. Can you explain those steps?


John Horn: The first step is to pay attention to what competitors say and do by downloading earnings calls or annual reports and scanning media releases. 


The second step is to find out what assets, resources, and capabilities they have. They may have a supply chain in markets or geographies that you don’t have or upgraded facilities. That’s where you start to differentiate the competitor. The way I like to phrase it is, “If I had their toys to play with, what would I do?”


The third step is to consider the person making the decisions. What do you know about them? When someone with a marketing background becomes the CEO, they won’t suddenly start optimizing the footprint of factories. That person will likely focus on marketing to help the company grow, partly because they will think, “My background is why the board hired me.”


"Companies doing competitive intelligence often try to collect all data about all competitors all at once, but they lack the staff or the ability to analyze the information and develop insights. You should start small. Who are the major competitors you want to track, and what do you want to track about them?"


Compa

The fourth element, which is really important, is making a prediction and then tracking it to see how it lines up with what happens. If you paid attention to what the competitor said and did, considered all its assets, and understood the leaders’ backgrounds, you can say, “I think they will do this in the next three to six months.” If what they do is in line with what you expected, you know you are on the right track. If you’re off, then go back and ask, “What did I miss? Maybe they used a certain partner or hired a new person to make decisions.” That updates what you pay attention to going forward to help you make better predictions. The objective is never to be 100 percent accurate, but it’s a lot better to be 30 percent accurate than to be 0 percent accurate in predicting what your competitor will do.


Emma Gibbs: In our practice, we talk a lot about the social side of strategy—ingrained ways of thinking or internal agendas. How should individuals who gather these insights share them with senior leaders so they make the best decisions?


John Horn: It’s more about storytelling than about charts and spreadsheets. It’s hard to say, “Here is what we should have invested, and here’s what the return would have been.” Rather, come up with examples of where the company faced big challenges in the past. For example, it might be something like, “Remember when we entered Latin America and got hammered by that competitor and lost our $200 million investment?” Then go back and look at what the competitor had done before. “If we had spent time to analyze the competitor, we may not have perfectly predicted their reaction, but we could have seen them as a threat and paid more attention to them.” Anecdotes aren’t proof, but you want those big hairy, messy anecdotes to be in senior leaders’ minds, so they realize that this is something they need to pay attention to.


Additionally, when you start implementing a competitive intelligence program, you want to go for small wins to build up the right to get bigger. Focus on one or two competitors and one or two strategic choices; maybe it’s one competitor’s pricing and product portfolio. Then track that and show that you can predict their actions with growing accuracy.


Emma Gibbs: Should business leaders start small when venturing in a new strategic direction, to get a sense of the competitor response?


John Horn: Some strategic choices can’t be made incrementally. If I’m going to acquire a company or expand a facility, I can’t do that partially over time. On the other hand, maybe I can expand a product into a couple of areas and see if a competitor tries to block it. We have this idea that our competitors are tracking our every move and just waiting to pounce. In fact, many will not realize that you are making a move in the market for weeks or months. With big moves, war games and simulations can be very helpful: “Emma, I want you to play a competitor. Here’s what I want to do. What would you do in response?” You can come up with a pretty accurate sense of the competitor response and based on that, decide what you might do differently.


Emma Gibbs: You interviewed people from different backgrounds for the book. What insights did you gain from that?


John Horn: One of the challenges with competitive insight is that you can’t talk to your competitor, so you have to intuit outside in, from second- and third-party resources. A colleague of mine said, “It’s similar to a homicide detective who can’t ask the victim, ‘Who killed you?’” It made me realize that other professionals, such as archaeologists, paleontologists, and neonatal ICU nurses, face the same challenge. Paleontologists can dig up fossil bones, but that doesn’t tell them how T. Rex ran or if it hunted in packs. I asked these different groups how they approach the challenge of not being able to directly interrogate the subject of their research and synthesized their answers into ten lessons. 


The number one thing was to create a diverse team. Almost everyone said, “If you want to have good competitive insight, you need people who look at the problem from different angles, whether that’s cultures, genders, or functional or educational backgrounds. The more people with different voices actively participating, the better answers you will get.”


Emma Gibbs: What advantages can technology bring to getting into the minds of competitors? And how do you combine it with the human aspects?


John Horn: It’s hard to do competitive insight without having a good database of what companies have done in the past and are doing currently so you can see patterns. Competitive insight or business analytics dashboards are very helpful for collecting and codifying that information. When you’re trying to predict your competitor’s actions, you need to know two things. 


First, are they following patterns? For pattern tracking, artificial intelligence and dashboards are helpful as long as AI recognizes the pattern. For example, if I want to know how a competitor will react when I change my prices or when another competitor changes its prices, I have to train the AI tool not only to recognize when the competitor I’m tracking changes its prices, but when others in the industry change their prices. I’m not sure AI has reached the level of being systemic and holistic in tracking multiple competitors relative to one other.


‘Knowing competitors’ historical patterns doesn’t tell us whether those patterns will continue. That’s where the human element comes in. Given what you know about what the competitors are saying and doing, do you expect them to continue those patterns or change direction because they want different outcomes?


The second element, which AI is not good at right now, is predicting changes. I used to do an exercise with my students to prepare them for consulting interviews. It was a consumer goods case, where one company’s market share went from 33 percent to 30 to 27, and another’s went from 27 percent to 30 to 33. The client company stayed flat. I asked the students which company they should be worried about, the one whose market share went up or the one whose market share went down. Most said the one that went up, because that competitor got stronger. I asked, “Do we know that this company wants its market share to continue to go up, or are they happy with where it is? Or did their market share go up without them doing anything and it will fall back because they’re not paying attention to it?” Similarly, the company whose market share went down could continue to go down because the company plans to exit that market. Alternately, its leaders could aggressively try to regain that market share, or they could try to merely stabilize things because they’re focused on other areas.

This shows that knowing historical patterns doesn’t tell us whether those patterns will continue or change. That’s where psychology and the human element comes in. Given what you know about what the competitors are saying and doing, do you expect them to continue those patterns or change direction because they want different outcomes? Technology is not yet good enough to make and track predictions without that human intervention.


Emma Gibbs: On your point about codifying patterns, what information should companies track?


John Horn: It’s a good question because companies doing competitive intelligence often try to do everything. They collect all data about all competitors all at once but lack the staff or the ability to analyze the information and develop insights. It goes back to my earlier point that when you start a competitive insights function, you should start small. Who are the major competitors you want to track and what do you want to track about them? Is it about product innovation, pricing moves, acquisitions, talent management, or supply chain? You figure that out by asking people in the organization where they have seen the competitor surprise or challenge them in terms of their response.


Coming up with a small set of competitors and strategic factors will help you focus the data collection. If I’m focusing on acquisitions, I don’t need to reverse-engineer the competitor’s product portfolio, but I do need to track how often they made acquisitions. How big were the deals? Were they bolt-ons? As you build up that information, you start to get feedback. “That’s great. Can you also look at this other question?” You earn the right to expand the function. It’s similar to the idea of a minimum viable product for entrepreneurs. You start with the MVP, get feedback from the people in your organization, and continue to develop and refine it.


Emma Gibbs: Does John Nash’s equilibrium theory, and game theory in general, play a role in predicting competitors’ moves?


John Horn: The Nash equilibrium essentially says, “I’m going to make the best response in reaction to you, and you will make the best response in reaction to me.” But Nash and the theory of economics always starts from the idea that we want to maximize profit. In the real world, sometimes we are trying to maximize market share in the hopes of getting profits down the road, or to maximize short-term earnings, or to integrate an acquisition as quickly as possible. All Nash would say is, “If that’s your objective, will the competitors have the same response as in other scenarios?” It’s a reminder that we have to consider not what we want other players to do, but what those players will do that’s in their own best interests.


Emma Gibbs: Can you offer some examples of organizations that have effectively generated competitive insights?


John Horn: One financial services company gained support from the CEO. Anytime someone proposed a new strategic initiative, the CEO would ask, “Did you talk to the competitive insight group?” Working with that group became part of how the company did business. The company was also deliberate in ensuring that information flowed up and down throughout the organization.


In another company I worked with, regional managers initially didn’t talk to each other. Since competitors applied similar strategies across regions, there were a lot of missed opportunities. The competitive insights group asked the regional managers to provide information about their main competitors and shared with them information they received from others. When the first reports started trickling in, the managers realized that when a competitor lowered prices in one country, they might lower prices in their regions. The company started small and then created a virtuous feedback loop: “If you want more information, give us more information.” Once you start providing information that helps people do their job better, they will want more of it.


Emma Gibbs: Do you find that companies tend to prioritize customer insights over competitive insights? Are there techniques used for customer research that could be applied to generating competitive insights?

John Horn: I think companies do a better job of understanding customers than competitors. I have led strategy workshops where we asked the client what makes the company distinctive, and the participants said things like high quality and customer responsiveness. The CEO said, “All our competitors would say the same thing, so how are we different?” We have to apply those same empathetic techniques to competitors, as well as supply chain partners and ecosystem stakeholders. For example, why are regulators making the moves they are making? Companies are good at scanning social media to understand how customers are responding to their products, but they don’t apply those techniques to reviewing how customers respond to competitors’ products. What is the competitor’s customer experience score? Are positive or negative responses changing for competitors? That can help you gain insight into what the competitor will do.


https://tinyurl.com/4udtzde2

воскресенье, 21 марта 2021 г.

Enduring Ideas: Portfolio of initiatives

 


The portfolio-of-initiatives framework offers a way to develop strategy in a more fluid, less predictable environment.

Classic approaches to business strategy assume a foreseeable future based on reasonable assumptions about developments in markets, technologies, or regulation. In an increasingly uncertain world, this approach falls short. The portfolio-of-initiatives framework, developed in the early 2000s by McKinsey director Lowell Bryan, draws on ideas such as the three horizons of growth and Hugh Courtney’s levels of uncertainty1 and offers a way to develop strategy in a more fluid, less predictable environment. In the article “Just-in-time strategy for a turbulent world,” Bryan compares such a portfolio to a convoy of ships in wartime: their numbers and diversity improve the likelihood of survival for any one of them.

The framework takes into consideration two aspects of initiatives: familiarity and time. Initiatives that allow a company to deploy a larger amount of distinctive knowledge than its competitors have give it the advantage of familiarity and the possibility of reaping superior rewards for a given level of risk. Such initiatives warrant the largest commitment of resources. Next come initiatives that require a company to acquire certain kinds of knowledge. In developing initiatives over time, a company must have enough of them not only to ensure large current returns but also to place bets that could help it grow in the medium and long terms.

Interactive
Enduring Ideas: The Porfolio of Initiatives
In this interactive presentation—one in a series of multimedia frameworks—McKinsey director Lowell Bryan talks about the origins of the portfolio-of-initiatives framework. Developed to address the need for strategy in a more fluid, less predictable environment, this approach treats strategies as actions that require continual monitoring and evaluation.

Introduction


Levels of familiarity/risk


Time


Potential market capitalization at stake


Implementing the portfolio of initiatives


Reading clusters


Relevance in a crisis



To apply the portfolio-of-initiatives approach, companies must take three steps: undertake a disciplined search for a number of initiatives that provide high rewards for the risks taken; monitor the resulting portfolio rigorously, reinvesting in successes and terminating failures; and take a flexible, evolutionary approach that allows for midcourse corrections. The resulting strategy, like a conscious form of natural selection, identifies the strongest initiatives and sheds the rest. The increasing uncertainty of today’s business environment and the importance of balancing risks with rewards make the portfolio-of-initiatives framework more relevant than ever.

https://mck.co/3vKa58r

пятница, 6 октября 2017 г.

The Flare and Focus of Successful Futurists


The ability to plausibly forecast the future requires alternating between broad and narrow ways of thinking.



Futurists are skilled at listening to and interpreting signals, which are harbingers of what’s to come. They look for early patterns — pretrends, if you will — as the scattered points on the fringe converge and begin moving toward the mainstream. The fringe is that place where hackers are experimenting, academics are testing their ideas, technologists are building new prototypes, and so on. Futurists know most patterns will come to nothing, so they watch and wait and test the patterns to find those few that will evolve into genuine trends. Each trend is a looking glass into the future, a way to see over time’s horizon. This is the art of forecasting the future: simultaneously recognizing patterns in the present and thinking about how those changes will impact the future so that you can be actively engaged in building what happens next — or at least be less surprised by what others develop. Futures forecasting is a learnable skill, and a process any organization can master.
Joseph Voros, a theoretical physicist and senior lecturer in strategic foresight at Swinburne University of Technology in Melbourne, Australia, offers my favorite explanation of futures forecasting, saying it informs strategy making by enhancing the “context within which strategy is developed, planned, and executed.”1 The advantage of forecasting the future in this way is obvious: Organizations that can see trends early can better prepare to take advantage of them. They can also help shape the broader context, with an understanding of how developments in seemingly unconnected industries will affect them. Most organizations that track emerging trends are adept at conversing and collaborating with those in other fields to plan ahead.
Although futures studies is an established academic discipline, few companies employ futurists. That’s starting to change as more leaders become familiar with the work futurists do. Accenture, Ford, Google, IBM, Intel, Samsung, and UNESCO all have had futurists on staff, and their work is quite different from what happens within the traditional research and development (R&D) function.
The futurists at these organizations know that their tools are best used within a group — and that the group’s composition matters tremendously to the outcomes they produce. Here’s why. Within every organization are people whose dominant characteristic is either creativity or logic. If you’ve been on a team that included both groups and didn’t have a great facilitator during your meetings, your team probably clashed. If it was an important project and there were strong personalities representing each side, the creative people felt as though their contributions were being discounted, while the logical thinkers — whose natural talents lie in managing processes, projecting budgets, or mitigating risk — felt undervalued because they weren’t coming up with bold new ideas. Your team undoubtedly had a difficult time staying on track, or worse, you might have spent hours meeting about how to have your next meeting. I call this the “duality dilemma.”
The duality dilemma is responsible for a lack of forward thinking at many organizations. It contributed to the decline of BlackBerry Ltd.’s smartphone business; the company (formerly known as Research in Motion Ltd.) never had an executable plan to remake the phone’s form factor and operating system in the age of the iPhone. Right-brained creatives wanted to make serious changes to the phone, while left-brained process thinkers were fixated on risk and maintaining BlackBerry’s customer base.2 The future of the business hinged on the company’s ability to bring both forces together to forecast trends and plan for the future.
BlackBerry’s experience suggests that forecasting the future of a product, company, or industry should neither be relegated to inventive visionaries nor mapped entirely by left-brain thinkers. Futures forecasting is meant to unite opposing forces, harnessing both wild imagination and pragmatism.

Turning a Dilemma Into a Dynamic

Overcoming the duality dilemma — and getting full use of both your creative- and logic-oriented team members — in order to track emerging trends and forecast the future is possible. But counterintuitively, it’s a matter of highlighting — rather than discouraging or downplaying — the strengths of each side. Stanford University’s Hasso Plattner Institute of Design (also known as the d.school) teaches a brainstorming technique that addresses the duality dilemma and illuminates how an organization can harness both strengths in equal measure by alternately broadening (“flaring”) and narrowing (“focusing”) its thinking.3
When a team is flaring, it is finding inspiration, making lists of ideas, mapping out new possibilities, getting feedback, and thinking big. When it is focusing, those ideas must be investigated, vetted, and decided upon. Flaring asks questions such as: What if? Who could it be? Why might this matter? What might be the implications of our actions? Focusing asks: Which option is best? What is our next action? How do we move forward?
The forecasting method I have developed — one, of course, influenced by other futurists but different in analysis and scope — is a six-step process that I have refined during the past decade as part of my work at the Future Today Institute. The first four steps involve finding a trend, while the last two steps inform what action you should then take. (See “A Six-Step Forecasting Methodology.”)

пятница, 7 июля 2017 г.

Ten Ways Big Data Is Revolutionizing Marketing And Sales

  • Customer Analytics (48%), Operational Analytics (21%), Fraud and Compliance (12%) New Product & Service Innovation (10%) & Enterprise Data Warehouse Optimization (10%) are among the most popular big data use cases in sales and marketing.
  • Customer Value Analytics (CVA) based on Big Data is making it possible for leading marketers to deliver consistent omnichannel customer experiences across all channels.
Of the hundreds of areas big data and analytics will revolutionize marketing and sales, the following is an overview of those that are delivering results today. How prices are defined, managed, propagated through selling networks and optimized is an area seeing rapid gains.  Attaining price optimization for a given product or service is becoming more possible thanks to advances in big data algorithms and advanced analytics techniques. Streamlining routine pricing decisions in commodity-driven industries where products are inelastic is also happening today.
An Overview Of Big Data’s Many Contributions To Marketing And Sales
Increasing the quality of sales leads, improving the quality of sales lead data, improving prospecting list accuracy, territory planning, win rates and decision maker engagement strategies are all areas where big data is making a contribution to sales today.
In marketing, big data is providing insights into which content is the most effective at each stage of a sales cycle, how Investments in Customer Relationship Management (CRM) systems can be improved, in addition to strategies for increasing conversion rates, prospect engagement, conversion rates, revenue and customer lifetime value. For cloud-based enterprise software companies, big data provides insights into how to lower the Customer Acquisition Cost (CAC), Customer Lifetime Value (CLTV), and manage many other customer-driven metrics essential to running a cloud-based business.
The following are the ten ways Big Data is revolutionizing marketing and sales:
  1. Differentiating pricing strategies at the customer-product level and optimizing pricing using big data are becoming more achievable. McKinsey found that 75% of a typical company’s revenue comes from its standard products and that 30% of the thousands of pricing decisions companies make every year fail to deliver the best price. With a 1% price increase translating into an 8.7% increase in operating profits, assuming there is no loss of volume, pricing has significant upside potential for improving profitability.  Source:Using big data to make better pricing decisions.

  1. Big data is revolutionizing how companies attain greater customer responsiveness and gain greater customer insights. A Forrester study found that 44% of B2C marketers are using big data and analytics to improve responsiveness to 36% are actively using analytics and data mining to gain greater insights to plan more relationship-driven strategies. Source: Marketing’s Big Leap Forward Overcome The Urgent Challenge To Improve Customer Experience And Marketing Performance (PDF).

  1. Customer Analytics (48%), Operational Analytics (21%), Fraud and Compliance (12%) New Product & Service Innovation (10%) and Enterprise Data Warehouse Optimization (10%) are among the most popular big data use cases in sales and marketing. A recent study by DataMeer found customer analytics dominate big data use in sales and marketing departments, supporting the four key strategies of increasing customer acquisition, reducing customer churn, increasing revenue per customer and improving existing products. Source: Big Data: A Competitive Weapon For The Enterprise

  1. Supported by Big Data and its affiliated technologies, it’s now possible to embed intelligence into contextual marketing. The marketing platform stack in many companies is growing fast based on evolving customer, sales, service and channel needs not met with existing systems today. As a result, many marketing stacks aren’t completely integrated at the data and process levels.  Big data analytics provides the foundation for creating scalable Systems of Insight to help alleviate this problem.  The following graphic is from the Forrester study made available for free download on the SAS site, Combine Systems Of Insight And Engagement For Contextual Marketing Tools And Technology: The Enterprise Marketing Technology Playbook.



  1. Forrester found that big data analytics increases marketers’ ability to get beyond campaign execution and focus on how to make customer relationships more successful.By using big data analytics to define and guide customer development, marketers increase the potential of creating greater customer loyalty and improving customer lifetime. The following graphic is from the SAS-sponsored Forrester study How Analytics Drives Customer Life-Cycle Management Vision: The Customer Analytics Playbook (PDF).

  1. Optimizing selling strategies and go-to-market plans using geoanalytics are starting to happen in the biopharma industry. McKinsey found that biopharma companies typically spend 20% to 30% of their revenues on selling, general, and administrative If these companies could more accurately align their selling and go-to-market strategies with regions and territories that had the greatest sales potential, go-to-market costs would be immediately reduced. Source:  Making Big Data Work: Biopharma, McKInsey & Company.

  1. 58% of Chief Marketing Officers (CMOs) say search engine optimization (SEO) and marketing, email marketing, and mobile is where big data is having the largest impact on their marketing programs today. 54% believe that Big Data and analytics will be essential to their marketing strategy over the long-term. Source: Big Data and the CMO: What’s Changing for Marketing Leadership?

  1. Market leaders in ten industries Forbes Insights tracked in a recent survey are gaining greater customer engagement and customer loyalty through the use of advanced analytics and Big Data. The study found that across ten industries, department-specific analytics and Big Data expertise were sufficient to get strategies off the ground and successful; enterprise-wide expertise and massive culture change was accomplished after pilot programs delivered positive results. Source: Forbes Insights, The Rise of The New Marketing Organization.

  1. Big Data is enabling enterprises to gain greater insights and actionable intelligence into each of the key drivers of their business. Generating revenue, reducing costs and reducing working capital are three core areas where Big Data is delivering business value today.  An enterprises’ value drivers scale more efficiently when managed using advanced analytics and Big Data.  The following value tree or roadmap to value illustrates this point.  Source: Big Data Stats from Deloitte.

  1. Customer Value Analytics (CVA) based on Big Data is making it possible for leading marketers to deliver consistent omnichannel customer experiences across all channels.CVA is emerging as a viable series of Big Data-based technologies that accelerate sales cycles while retaining and scaling the personalized nature of customer relationships. The bottom line is that CVA is now a viable series of technologies for orchestrating excellent omnichannel customer experiences across a selling network. Source:   CapGemini Presentation, From Customer Insights to Action Ruurd Dam, November 2015.