3 items tagged "communication"

  • Communicating competitive intelligence insights in 5 steps

    Communicating competitive intelligence insights in 5 steps

    You work hard to stay on top of your competitors’ moves. You sift through big and small changes alike and analyze the data to identify trends and strategies. But then what do you do with the data? One of the biggest challenges with making use of competitive intelligence (CI) is distributing that intel to the relevant stakeholders within your organization so that your team can take action on your competitive insights. 

    The teams who are most successful at communicating competitive intelligence have identified relevant communication channels, established a regular cadence for distribution, and, of course, crafted their CI updates to deliver immense value in a compact package.

    Borrowing from the ideas of great CI updates, we’ve created a template for distributing competitive intelligence. Read on for recommendations to create your own.

    1: Tailor Deliverables to Your Stakeholders 

    The first step is to identify your competitive intelligence stakeholders. Each stakeholder within your organization has their own set of priorities. Because each team within your organization is responsible for their own objectives, the information that you will be delivering to each team will differ in type of content, deliverable type, and distribution channel. No single CI report or channel will work for every audience, so think about how you can customize your update to each group.

    Sales wants to know how to win more deals  and position your solution against alternatives. An example of a great deliverable for your sales team is a competitive battlecard, which can be updated in real-time, and lives either in your CRM or competitive intelligence platform. 

    Marketing wants to know how to create differentiated messaging, impactful content, and stand out against competitors in a crowded market. An example of a great deliverable for marketing is a competitive landscape snapshot or a competitive newsletter that highlights your competitor’s key messaging changes, market information, and latest content or campaign information. 

    Product wants to know what your competitors’ are doing to improve their own offerings and solve your market's problems. An example of a deliverable for your product team is a product sheet (or a one-pager, if you want to give a product overview). This allows your product team to gain insight into features, benefits, product updates, and even feedback from your competitor’s customers, which will then help your product team iterate on their strategy. 

    Executives don’t have a lot of time to dedicate to CI, so the simpler the deliverable, the better. An example of a great deliverable for your executive team is an executive dashboard. This is impactful for your executives to get a snapshot of major market shifts happening in real-time. Think of this as a command center for your executives to keep a pulse on the market.

    While these are just some examples of competitive intelligence deliverables, it gives you insight into how you can tailor your CI deliverables to each stakeholder within your organization

    2: Make it Digestible

    No matter which deliverable you’re creating or who you’re communicating it to, you want to ensure that you’re making the content digestible. There is a lot of competitive intelligence data out there, and it’s easy to get lost in competitive intel. That can lead to your team not having enough time to act on the intel or even worse, starting to ignore intel.

    A great way to make sure your entire organization is getting competitive intelligence delivered to them is by creating a competitive intel digest or newsletter. These newsletters should be digestible to encourage others to consistently review and evaluate the findings. Keep the digests short and focused and leverage formatting as well to keep the content skimmable. These can be sent out at whatever cadence works well for your team, whether that be daily, weekly, or monthly. 

    3: Answer “So What”

    In order for competitive intel to have an impact on your team, you need to help bridge the gap between what happened and why it matters. With every piece of intel you plan to share, ask and answer the question, “So what?” because that’s exactly what others will be thinking. Think of each piece of intel going through the following path: What Happened -> What It Means / Why It Matters -> What We Should Do About It. 

    The worst thing that happens with competitive intelligence information is that it doesn’t get used. This often happens when the “so what” goes unanswered. When you’re creating your competitive intelligence deliverables, you want to be sure that there is a purpose behind every insight you’re including. The more impact an insight has, the more likely your team is to leverage that information. 

    4: Keep up a Regular Cadence of Communication

    Competitive intelligence is like eating healthy or going to the gym - you need to do it consistently over a long period of time to see the impact. That means whatever cadence you choose for CI should be maintained. This allows you to take advantage of both short-term opportunities and long-term trends. If you’re not sure how often to leverage each method of communication, here is a simple list. 

    Email - Daily, Weekly, or Monthly 

    Meetings - Weekly or Monthly 

    Chat App / Slack - Daily 

    Wiki / Intranet - Weekly or Monthly 

    CRM or Competitive Intelligence Platform - Daily 

    5: Look at the Short-Term and Long-Term Objectives

    Competitive Intelligence isn’t a one and done type of initiative. CI is a long-term, ongoing process. While there are many short-term goals that can be accomplished with CI, there are long term wins as well. With a particular CI update cadence, you can risk boxing yourself into the intel delivered in that timeframe. Be sure to take a step back and identify longer-term trends to watch, and take a deep dive into the long-term view from time to time.

    If you want to make an impact with your competitive intelligence program, ensure that you’re effectively communicating your data across your organization. Following these suggestions, you’ll be able to tailor CI to your stakeholders, create impactful deliverables, and communicate in a timely manner. Once your entire organization has a pulse on the competition, you’ll be able to level-up your strategy and gain a strong competitive advantage. 

    Source: Crayon

    Author: Emily Dumas



  • Helping your employees deal with change by communicating it the right way

    Helping your employees deal with change by communicating it the right way

    Last year, 70% of employees we surveyed indicated that they faced an increasing amount of change over the prior year. In addition, Communications executives identified employee change fatigue as the most pressing challenge they faced. Of course, 2020 was a year unlike any other, but before attributing these figures to the dramatic events of that year, consider this: from 2017 to 2019, employee change fatigue had already topped of the list of communications leaders’ challenges. Clearly, this is not a new phenomenon, and does not appear to be going away any time soon.

    VUCA Environment

    More and more leaders are using the term “VUCA” (volatile, uncertain, complex, and ambiguous) to describe the current environment that employees are facing. VUCA is an acronym coined by the US army in the 1980s, but over the past several years, it has migrated into business lexicon to describe a constantly ongoing state of change. For communicators, it can be instructive to frame the communications strategy that will effectively engage workers in this brave new world.

    Given this context then, how can communications leaders engage employees to help them navigate this ever-changing world? First, they need to reconsider the nature of the changes that employees are facing. But they must also acknowledge the limitations of the traditional approach to change communications, which prioritizes large-scale, organizational transformations like a merger or acquisition, the launch of a new corporate strategy, or introduction of a new CEO.

    While all of these events still necessitate a communications response, they represent a small fraction – just 4% – of the total number of changes that employees face in a given year. This leaves a gaping hole in the way that leaders help employees through the other 96% of changes – everything from rolling out new technology in the workplace to getting a new manager – that may be individually smaller in scope but collectively have the potential to be quite disruptive.

    Communications’ response: adopting an always-on strategy

    When we studied how communications leaders were most successfully responding to this environment, we found that they were fundamentally shifting how their employees think about changes; not as one-off events, but by talking about change as an ecosystem. In short, they were adopting an ‘always-on change strategy,’ where they deliver regular, overarching messages that are not specific to any one organizational change, but rather discuss how the organization operates in a consistently changing state.

    Broadly speaking, an always-on change strategy includes two components:

    • Providing employees with regular information about how decisions have been made and the implications of a VUCA environment on the organization
    • Providing employees with access to self-serve networks and resources that they can use for just-in time support and to build their resiliency

    Adopting an always-on strategy dedicated to supporting employees in an environment of ongoing change has several benefits when compared to the traditional, one-off approach:

    • First, the regular information about VUCA helps reset employee expectations of stability. Information that is initially shared with employees to help them understand change often becomes outdated as business conditions change. As such, the always-on strategy can help employees become better primed to expect that their organization is in a consistent period of change.
    • Second, visibility into how change decisions are made helps employees follow the organization’s change journey and prompts them to seek out opportunities to consider how they can contribute to that journey.
    • Third, access to self-serve networks and resources helps employees find long-term success in a VUCA environment, which ultimately builds resiliency.

    One final advantage of adopting an ‘always-on change strategy’ is that it helps to mitigate the need for communications leaders to respond to every single change that occurs across the organization. On the contrary, an effective always-on communications strategy has the potential to be self-sustaining, as it provides employees the ability to access self-serve networks and resources in real-time, drawing upon peer-to-peer engagement to help each other navigate the ongoing VUCA environment.

    Author: Emmett Fitzpatrick

    Source: Gartner

  • Why communication on algorithms matters

    Why communication on algorithms matters

    The models you create have real-world applications that affect how your colleagues do their jobs. That means they need to understand what you’ve created, how it works, and what its limitations are. They can’t do any of these things if it’s all one big mystery they don’t understand.

    'I’m afraid I can’t let you do that, Dave… This mission is too important for me to let you jeopardize it'

    Ever since the spectacular 2001: A Space Odyssey became the most-watched movie of 1968, humans have both been fascinated and frightened by the idea of giving AI and machine learning algorithms free rein. 

    In Kubrick’s classic, a logically infallible, sentient supercomputer called HAL is tasked with guiding a mission to Jupiter. When it deems the humans on board to be detrimental to the mission, HAL starts to kill them.

    This is an extreme example, but the caution is far from misplaced. As we’ll explore in this article, time and again, we see situations where algorithms 'just doing their job' overlook needs or red flags they weren’t programmed to recognize. 

    This is bad news for people and companies affected by AI and ML gone wrong. But it’s also bad news for the organizations that shun the transformative potential of machine learning algorithms out of fear and distrust. 

    Getting to grips with the issue is vital for any CEO or department head that wants to succeed in the marketplace. As a data scientist, it’s your job to enlighten them.

    Algorithms aren't just for data scientists

    To start with, it’s important to remember, always, what you’re actually using AI and ML-backed models for. Presumably, it’s to help extract insights and establish patterns in order to answer critical questions about the health of your organization. To create better ways of predicting where things are headed and to make your business’ operations, processes, and budget allocations more efficient, no matter the industry.

    In other words, you aren’t creating clever algorithms because it’s a fun scientific challenge. You’re creating things with real-world applications that affect how your colleagues do their jobs. That means they need to understand what you’ve created, how this works and what its limitations are. They need to be able to ask you nuanced questions and raise concerns.

    They can’t do any of these things if the whole thing is one big mystery they don’t understand. 

    When machine learning algorithms get it wrong

    At other times, algorithms may contain inherent biases that distort predictions and lead to unfair and unhelpful decisions. Just take the case of this racist sentencing scandal in the U.S., where petty criminals were rated more likely to re-offend based on the color of their skin, rather than the severity or frequency of the crime. 

    In a corporate context, the negative fallout of biases in your AI and ML models may be less dramatic, but they can still be harmful to your business or even your customers. For example, your marketing efforts might exclude certain demographics, to your detriment and theirs. Or that you deny credit plans to customers who deserve them, simply because they share irrelevant characteristics with people who don’t. To stop these kinds of things from happening, your non-technical colleagues need to understand how the algorithm is constructed, in simple terms, enough to challenge your rationale. Otherwise, they may end up with misleading results.

    Applying constraints to AI and ML models

    One important way forward is for data scientists to collaborate with business teams when deciding what constraints to apply to algorithms.

    Take the 2001: A Space Odyssey example. The problem here wasn’t that the ship used a powerful, deep learning AI program to solve logistical problems, predict outcomes, and counter human errors in order to get the ship to Jupiter. The problem was that the machine learning algorithm created with this single mission in mind had no constraints. It was designed to achieve the mission in the most effective way using any means necessary, preserving human life was not wired in as a priority.

    Now imagine how a similar approach might pan out in a more mundane business context. 

    Let’s say you build an algorithm in a data science platform to help you source the most cost-effective supplies of a particular material used in one of your best-loved products. The resulting system scours the web and orders the cheapest available option that meets the description. Suspiciously cheap, in fact, which you would discover if you were to ask someone from the procurement or R&D team. But without these conversations, you don’t know to enter constraints on the lower limit or source of the product. The material turns out to be counterfeit, and an entire production run is ruined.

    How data scientists can communicate better on algorithms

    Most people who aren’t data scientists find talking about the mechanisms of AI and ML very daunting. After all, it’s a complex discipline, that’s why you’re in such high demand. But just because something is tricky at a granular level, doesn’t mean you can’t talk about it in simple terms.

    The key is to engage everyone who will& use the model as early as possible in its development. Talk to your colleagues about how they’ll use the model and what they need from it. Discuss other priorities and concerns that affect the construction of the algorithm and the constraints you implement. Explain exactly how the results can be used to inform their decision-making but also where they may want to intervene with human judgment. Make it clear that your door is always open and the project will evolve over time, you can keep tweaking if it’s not perfect.

    Bear in mind that people will be far more confident about using the results of your algorithms if they can tweak the outcome and adjust parameters themselves. Try to find solutions that give individual people that kind of autonomy. That way, if their instincts tell them something’s wrong, they can explore this further instead of either disregarding the algorithm or ignoring potentially valid concerns.

    Final thoughts: shaping the future of AI

    As Professor Hannah Fry, author of Hello World: How to be human in the age of the machine,  explained in an interview with the Economist:

    'If you design an algorithm to tell you the answer but expect the human to double-check it, question it, and know when to override it, you’re essentially creating a recipe for disaster. It’s just not something we’re going to be very good at.

    But if you design your algorithms to wear their uncertainty proudly front and center, to be open and honest with their users about how they came to their decision and all of the messiness and ambiguity it had to cut through to get there, then it’s much easier to know when we should trust our own instincts instead'.In other words, if data scientists encourage colleagues to trust implicitly in the HAL-like, infallible wisdom of their algorithms, not only will this lead to problems, it will also undermine trust in AI and ML in the future. 

    Instead, you need to have clear, frank, honest conversations with your colleagues about the potential and limitations of the technology and the responsibilities of those that use it, and you need to do that in a language they understand.

    Author: Shelby Blitz

    Source: Dataconomy

EasyTagCloud v2.8