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среда, 2 августа 2023 г.

Benchmarking your digital marketing capability

 

Using capability maturity models to audit your digital maturity and set targets to improve digital marketing effectiveness

We've been adding to our visual tools to help all members assess how well their businesses are adapting to using digital media and technology and to set targets to improve their results from digital marketing.

We have collected these visuals together in a single download so that you can easily review them and print the most relevant for you. We've designed them So members can use them for different scales of business and roles. There are more than 10 templates which cover:

  • Digital marketing for small and medium businesses using our RACE framework
  • Digital transformation for larger businesses
  • Digital channel marketing activities including SEO, Social media, email and content marketing

You can see one example, which I designed for reviewing digital marketing effectiveness with senior leaders in small and medium or larger businesses. The other templates are more granular looking at specific digital marketing activities using our RACE framework.


Members can also use our Capability graders, which are free, interactive versions enabling you to compare your score to other members (anonymously) and get recommendations on which resources can help you improve your score.

Of course, capability graders and improvement recommendations are most useful to businesses that are actively trying to improve their digital marketing strategies. If you're still looking for buy-in for digital marketing activities or optimization, you could start by reading up on '10 reasons why you need a digital marketing strategy'.

What is the 5 point benchmarking scale based on?

In this article I'll explain the background to these capability reviews - I have to go back a while since I first became aware of the benefits of doing this type of process benchmarking back in the early 1990s!

Do you know the Carnegie Mellon Capability Maturity model (CMM)? That's where my inspiration for benchmarking businesses for digital marketing originally came from. It’s likely that you don’t, if you work in marketing, unless your background is similar to mine.

I used to manage software development back in the day, before the web, yes that long ago…

Back then I used to manage small teams to create packaged software used by thousands of engineers worldwide, so it was important that we minimized defects when we shipped a new release. Of course, every major bug irritates customers and generates support and rework.

So the team leaders and I worked hard to implement a quality management system process for creating new software updates to minimize bugs - many who are involved with managing updates to web and E-commerce sites will be familiar with requirements specs, prototypes, and testing schedules, although this was before Agile and Scrum.

As part of trying to improve our development processes, we used to find it useful to apply capability maturity models to benchmark against competitors. They help you be more objective about your capabilities and know where improvements are needed. In the classic CMM model there are 5 or 6 clearly defined stages as shown below:



The story behind developing these digital marketing maturity benchmark tools

When I switched from software development to marketing to lecturing in the business school in the University of Derby around 1995, the web was in its infancy and there were a lot more problems with managing site performance and content than there are today. Remember those quaint “under construction” signs. Laughable now!

Many managing the adoption of digital technologies by their companies were based with a similar problem to the software developers. They needed to develop a robust, repeatable process that would enable them to deliver a service that was effective both for their customers and their commercial goals. Many still do. So this is where reviewing your capabilities can help.

If you're new to digital marketing, don't forget to check our top 18 recommended digital marketing techniques by asking 'what is digital marketing?'.

Using benchmarking or scoring of your digital maturity can help:

  • 1 Audit current approaches to digital marketing to identify areas for improvement;
  • 2 Benchmark against competitors who are in the same market sector;
  • 3 Identify best practice from more advanced adopters;
  • 4 Set targets and develop strategies and roadmaps for improving capabilities through time;
  • 5 Communicate the current situation to colleagues budget holders and highlight investment priorities in for different activities.

This need for well-managed processes is still the case, particularly with ongoing developments in the technology for delivering customer experiences across mobile and desktop and the need to integrate content and social media from multiple sources. Given that digital marketing is “Always-on”, it makes sense to benchmark the overall capability of digital marketing using a simple scoring system.

I used to participate in Workshops at Cranfield School of Management where capability models developed by Professor Hugh Wilson were reviewed with companies participating in a benchmarking group. This rang a bell, so it gave me the idea to apply what I had learned of CMM for software development and apply it.

Benchmarking frameworks for Smart Insights Business Members

I originally developed capability benchmark spreadsheets on personal consulting projects for brands like Barclaycard, BP and Mercedes Benz where I interviewed stakeholders asking them to assess their digital capabilities on a detailed scale.

A version of this was referenced later in the Econsultancy Managing E-commerce Teams reports I worked on in 2005 and 2008 and more recently have updated them to the Smart Insights Digital marketing strategy audit which is structured around the RACE Planning framework - it's where we recommend Expert members start their improvements to digital marketing.

We also have an online retail capability benchmarking audit by Chris Jones. I got in touch with Chris since I admired the auditing approach in his Multichannel Retail Handbook and we arranged to share it with Smart Insights members.

Free Interactive Benchmarking tool

After developing many digital benchmarking spreadsheets and marketing strategy audits, I wanted to take digital benchmarking to the next level by having an interactive tool that could be used to score a business digital marketing capabilities and make recommendations to improve.

That's what our interactive digital strategy benchmarking tool does. By scoring your business capabilities across all areas of the RACE planning framework you will be given a score and recommended resources and e-learning models to help you improve your business capability to use digital marketing effectively.

By Dave Chaffey

https://www.smartinsights.com/





суббота, 15 июля 2023 г.

15 Applications of Artificial Intelligence in Marketing

 

Mapping the most effective AI technologies for marketing across the customer lifecycle in the coming year

Artificial Intelligence (AI) technology is a hot topic in marketing at the moment, with the huge interest in ChatGPT (see our articles on using ChatGPT for marketing).

But AI is a broad term covering a wide range of different technologies, many of which have been available for some time. We originally wrote this article based on the opportunities for using AI for marketing in 2017 and have updated it since with examples.

Artificial intelligence means any technology that seeks to mimic human intelligence, which covers a huge range of capabilities such as voice and image recognition, machine learning techniques and semantic search.

That's why, in our AI and Machine Learning briefing for members, we have identified fifteen artificial intelligence techniques that businesses of all sizes can implement, rather than techniques that only major tech giants can devote resources to. We've plotted the techniques across the customer lifecycle so you can see how each AI tactic can help take your customers down the marketing funnel.


All the techniques are 'AI' in the sense that they involve computer intelligence, but we've broken them down into 3 different types of technology - Machine learning techniquesapplied propensity models, and AI applications. Machine learning techniques involve using algorithms to 'learn' from historical data sets, which can then create propensity models. Applied propensity models are when these propensity models are put to work predicting given events- such as scoring leads based on their likelihood to convert. AI applications are other forms of AI, which do tasks one would usually associate with a human operator such as answering customer questions or writing new content.

Each different application has major implications for marketers, but the applications have different roles to play across the customer journey. Some are better for attracting customers, whilst others are useful for conversion or re-engaging past customers. That's why we've divided the techniques across the RACE framework.


If you're looking to apply technology to optimize your marketing strategy, our RACE Framework is the perfect structure for you to plan and assess your marketing activities. Through the journey of reach, act, convert, and engage, our RACE Framework empowers marketers and managers to apply customer insights and data to measure and prioritize their best-performing omnichannel touchpoints.

Reach - Attract visitors with a range of inbound techniques

Reach involves using techniques such as content marketing, SEO and other 'earned media' to bring visitors to your site and start them on the buyer's journey. AI and applied propensity models can be used at this stage to attract more visitors and provide those that do reach your site with a more engaging experience.

1. AI-generated content

This is a really interesting area for AI. AI can't write a political opinion column or a blog post on industry-specific best practice advice, but there are certain areas where AI-generated content can be useful and help draw visitors to your site.

For certain functions AI content writing programs are able to pick elements from a dataset and structure a ‘human-sounding’ article. An AI writing program called ‘WordSmith’ produced 1.5 billion pieces of content as early as 2016.

AI writers are useful for reporting on regular, data-focused events. Examples include quarterly earnings reports, sports matches, and market data. If you operate in a relevant niche such as financial services, then AI-generated content could form a useful component of your content marketing strategy. There are now solutions from vendors available and returning good results in copywriting for Facebook Ads, email subject lines and nurturing emails that we highlight in the latest edition of our AI guide.

2. Smart content curation

AI-powered content curation allows you to better engage visitors to your site by showing them content relevant to them. This technique is most commonly found in the 'customers who bought X also bought Y' section on many sites, but can also be applied to blog content and personalizing site messaging more widely. It's also a great technique for subscription businesses, where the more someone uses the service, the more data the machine learning algorithm has to use and the better the recommendations of content become. Think of Netflix's recommendation system being able to consistently recommend to you shows you'd be interested it.

3. Voice search

Voice search is another AI technology but, when it comes to using it for marketing, it's about utilizing the technology developed by the major players (Google, Amazon, Apple) rather than developing your own capability. Voice search will change future SEO strategies, and brands need to keep up. A brand that nails voice search will leverage gains in organic traffic with high purchase intent thanks to increased voice search traffic due to AI-driven virtual personal assistants.

4. Programmatic media buying

Programmatic media buying can use propensity models generated by machine learning algorithms to more effectively target ads at the most relevant customers. Programmatic ads need to get smarter in the wake of Google's brand safety scandal. It was revealed ads placed programmatically through Google's ad network were appearing on terrorist's websites. AI can help here by recognizing questionable sites and removing them from the list of sites ads can be placed on.

By increasing your reach through the strategic implementation of AI, you are filling the top of your marketing funnel and giving yourself the best chance of success in marketing. Find out how you can use the RACE Framework to win more customers.

Act - Draw visitors in and make them aware of your product

5. Propensity modelling

As already mentioned, propensity modelling is the goal of a machine learning project. The machine learning algorithm is fed large amounts of historical data, and it uses this data to create a propensity model which (in theory) is able to make accurate predictions about the real world. The simple diagram below shows the stages of this process.


6. Predictive analytics

Propensity modelling can be applied to a number of different areas, such as predicting the likely hood of a given customer to convert, predicting what price a customer is likely to convert at, or what customers are most likely to make repeat purchases.  This application is called predictive analytics because it uses analytics data to make predictions about how customers behave. The key thing to remember is that a propensity model is only as good as the data provided to create it, so if there are errors in your data or a high level of randomness, it will be unable to make accurate predictions.

7. Lead scoring

Propensity models generated by machine learning can be trained to score leads based on certain criteria so that your sales team can establish how 'hot' a given lead is, and if they are worth devoting time to. This can be particularly important in B2B businesses with consultative sales processes, where each sale takes a considerable amount of time on the part of the sales team. By contacting the most relevant leads, the sales team can save time and concentrate their energy where it is most effective. The insights into a lead's propensity to buy can also be used to target sales and discounts where they are most effective.


8. Ad targeting

Machine learning algorithms can run through vast amounts of historical data to establish which ads perform best on which people and at what stage in the buying process. Using this data they can serve them with the most effective content at the right time. By using machine learning to constantly optimize thousands of variables you can achieve more effective ad placement and content than traditional methods. However, you'll still need humans to do the creative parts!

By increasing customer interaction on your site through the strategic implementation of AI, you are nurturing cold traffic to increase their likelihood of converting. Find out how you can use the RACE Framework to win more customers.

Convert - nudge interested consumers into becoming customers

9. Dynamic pricing

All marketers know that sales are effective at shifting more product. Discounts are extremely powerful, but they can also hurt your bottom line. If you make twice as many sales with a two-thirds smaller margin, you've made less profit than you would have if you didn't have a sale.

Sales are so effective because they get people to buy your product that previously wouldn't have considered themselves able to justify the cost of the purchase. But they also mean people that would have paid the higher price pay less than they would have.

Dynamic pricing can avoid this problem, by targeting only special offers only at those likely to need them in order to convert. Machine learning can build a propensity model of which traits show a customer is likely to need an offer to convert, and which are likely to convert without the need for an offer. This means you can increase sales whilst not reducing your profit margins by much, thus maximizing profits.

10. Web and app personalisation

Using a propensity model to predict a customer's stage in the buyer's journey can let you serve that customer, either on an app or on a web page, with the most relevant content. If someone is still new to a site, content that informs them and keeps them interested will be most effective, whilst if they have visited many times and are clearly interested in the product then more in-depth content about a product's benefits will perform better.

11. Chatbots

Chatbots mimic human intelligence by being able to interpret consumer’s queries and complete orders for them. You might think chatbots are extremely difficult to develop and only huge brands with massive budgets will be able to use them. Actually, using open chatbot development platforms, it's relatively easy to create your own chatbot without a big team of developers.

Our AI guide highlights open source technologies from Facebook and Google which brands such as Dominos and KLM are using for developing their own chatbots for customer service.

12. Re-targeting

Much like with ad targeting, machine learning can be used to establish what content is most likely to bring customers back to the site based on historical data. By building an accurate prediction model of what content works best at winning back different types of customers, machine learning can be used to optimize your retargeting ads to make them as effective as possible.

Find out how you can increase your customer conversions today. Our RACE Framework gives you a marketing funnel structure integrated across your customers' experiences of your company to help you identify and prioritize high-converting journies. Book your call today to discover how RACE planning can help you drive the results you need

Engage - keep your customers returning

13. Predictive customer service

It's far easier to make repeat sales to your existing customer base than it is to attract new customers. So, keeping your existing customers happy is key to your bottom line. This is particularly true in subscription-based business, where a high churn rate can be extremely costly. Predictive analytics can be used to work out which customers are most likely to unsubscribe from a service by assessing what features are most common in customers who do unsubscribe. It's then possible to reach out to these customers with offers, prompts or assistance to prevent them from churning.

14. Marketing automation

Marketing automation techniques generally involve a series of rules, which when triggered initiative interactions with the customer. But who decided these rules? Generally, a marketer who's basically guessing what will be most effective. Machine learning can run through billions of points of customer data and establish when are the most effective times to make contact, what words in subject lines are most effective and much more. These insights can then be applied to boost the effectiveness of your marketing automation efforts.

15. 1:1 dynamic emails

In a similar fashion to marketing automation, applying insights generated from machine learning can create extremely effective 1:1 dynamic emails. Predictive analytics using a propensity model can establish a subscriber's propensity to buy certain categories, sizes and colours through their previous behaviour and displays the most relevant products in newsletters. The product stock, deals, pricing is all correct at the time of opening the email.

By Dave Chaffey

https://www.smartinsights.com/






суббота, 1 июля 2023 г.

What is a Decision Matrix? Criteria Rating Form, Weighted Ranking.

 DECISION MATRIX. Also called: Pugh matrix, decision grid, selection matrix or grid, problem matrix, problem selection matrix, opportunity analysis, solution matrix, criteria rating form, criteria-based matrix

A decision matrix evaluates and prioritizes a list of options and is a decision-making tool. The team first establishes a list of weighted criteria and then evaluates each option against those criteria. This is a variation of the L-shaped matrix.

WHEN TO USE A DECISION MATRIX

  • When a list of options must be narrowed to one choice
  • When the decision must be made on the basis of several criteria
  • After a list of options has been reduced to a manageable number by list reduction

Typical situations are:

  • When one improvement opportunity or problem must be selected to work on
  • When only one solution or problem-solving approach can be implemented
  • When only one new product can be developed

DECISION MATRIX PROCEDURE

  1. Brainstorm the evaluation criteria appropriate to the situation. If possible, involve customers in this process.
  2. Discuss and refine the list of criteria. Identify any criteria that must be included and any that must not be included. Reduce the list of criteria to those that the team believes are most important. Tools such as list reduction and multivoting may be useful.
  3. Assign a relative weight to each criterion, based on how important that criterion is to the situation. This can be done in two ways:
    1. By distributing 10 points among the criteria, based on team discussion and consensus.
    2. By each member assigning weights, then the numbers for each criterion for a composite team weighting. 
  4. Draw an L-shaped matrix. Write the criteria and their weights as labels along one edge and the list of options along the other edge. Typically, the group with fewer items occupies the vertical edge.
  5. Evaluate each choice against the criteria. There are three ways to do this:

    Method 1: Establish a rating scale for each criterion. Some options are:

    1. 1, 2, 3 (1 = slight extent, 2 = some extent, 3 = great extent)
    2. 1, 2, 3 (1 = low, 2 = medium, 3 = high)
    3. 1, 2, 3, 4, 5 (1 = little to 5 = great)
    4. 1, 4, 9 (1 = low, 4 = moderate, 9 = high)

    It is important that your rating scales are consistent. Word your criteria and set the scales so that the high end of the scale (5 or 3) is always the rating that would tend to make you select that option: greatest impact on customers, greatest importance, least difficulty, greatest likelihood of success.

    Method 2: For each criterion, rank-order all options according to how well each meets the criterion. Number them with 1 being the option that is least desirable according to that criterion.

    Method 3 (Pugh matrix): Establish a baseline, which may be one of the alternatives or the current product or service. For each criterion, rate each other alternative in comparison to the baseline, using scores of worse (-1), same (0), or better (+1). Finer rating scales can be used, such as 2, 1, 0, -1, -2 for a five-point scale or 3, 2, 1, 0, -1, -2, -3 for a seven-point scale. Again, be sure that positive numbers reflect desirable ratings.

  6. Multiply each option’s rating by the weight. Add the points for each option. The option with the highest score will not necessarily be the one to choose, but the relative scores can generate meaningful discussion and lead the team toward consensus

DECISION MATRIX EXAMPLE

Figure 1 shows a decision matrix used by the customer service team at the Parisian Experience restaurant to decide which aspect of the overall problem of "long wait time" to tackle first. The problems they identified are customers waiting for the host, the waiter, the food, and the check.

The criteria they identified are "Customer pain" (how much does this negatively affect the customer?), "Ease to solve," "Effect on other systems," and "Speed to solve." Originally, the criteria "Ease to solve" was written as "Difficulty to solve," but that wording reversed the rating scale. With the current wording, a high rating on each criterion defines a state that would encourage selecting the problem: high customer pain, very easy to solve, high effect on other systems, and quick solution.

Figure 1: Decision Matrix Example

"Customer pain" has been weighted with 5 points, showing that the team considers it by far the most important criterion, compared to 1 or 2 points for the others.

The team chose a rating scale of high = 3, medium = 2, and low = 1 and used it for the problem. "Customers wait for food." In this example, the customer pain is medium (2), because the restaurant ambiance is nice. This problem would not be easy to solve (low ease = 1), as it involves both waiters and kitchen staff. The effect on other systems is medium (2), because waiters have to make several trips to the kitchen. The problem will take a while to solve (low speed = 1), as the kitchen is cramped and inflexible.

Each rating is multiplied by the weight for that criterion. For example, "Customer pain" (weight of 5) for "Customers wait for host" rates high (3) for a score of 15. The scores are added across the rows to obtain a total for each problem. "Customers wait for host" has the highest score at 28. Since the next highest score is 18, the host problem probably should be addressed first.

DECISION MATRIX CONSIDERATIONS

  • A very long list of options can first be shortened with a tool such as list reduction or multivoting.
  • Criteria that are often used fall under the general categories of effectiveness, feasibility, capability, cost, time required, and support or enthusiasm (of team and of others). Other commonly used criteria include:

    For selecting a problem or an improvement opportunity:
    • Within control of the team
    • Financial payback
    • Resources required (e.g., money, people)
    • Customer pain caused by the problem
    • Urgency of problem
    • Team interest or buy-in
    • Effect on other systems
    • Management interest or support
    • Difficulty of solving
    • Time required to solve

    For selecting a solution:
    • Root causes addressed by this solution
    • Extent of resolution of problem
    • Cost to implement (e.g., money, time)
    • Return on investment; availability of resources (e.g., people, time)
    • Ease of implementation
    • Time until solution is fully implemented
    • Cost to maintain (e.g., money, time)
    • Ease of maintenance
    • Support or opposition to the solution
    • Enthusiasm by team members
    • Team control of the solution
    • Safety, health, or environmental factors
    • Training factors
    • Potential effects on other systems
    • Potential effects on customers or suppliers
    • Value to customer
    • Potential problems during implementation
    • Potential negative consequences

Additional considerations

  • While a decision matrix can be used to compare opinions, it is better used to summarize data that have been collected about the various criteria when possible.
  • Sub-teams can be formed to collect data on the various criteria.
  • Several criteria for selecting a problem or improvement opportunity require guesses about the ultimate solution. For example: evaluating resources required, payback, difficulty to solve, and time required to solve. Therefore, your rating of the options will be only as good as your assumptions about the solutions.
  • It’s critical that the high end of the criteria scale (5 or 3) always is the end you would want to choose. Criteria such as cost, resource use and difficulty can cause confusion (for example, low cost is highly desirable). Avoid this by rewording your criteria: Say "low cost" instead of "cost"; "ease" instead of "difficulty." Or, in the matrix column headings, write what generates low and high ratings. For example:

     

    Importance

    Cost

    Difficulty

    low = 1 high = 5

    high = 1 low = 5

    high = 1 low = 5

  • If individuals on the team assign different ratings to the same criterion, discuss until the team arrives at a consensus. Do not average the ratings or vote for the most popular one.
  • In some versions of this tool, the sum of the unweighted scores is also calculated and both totals are studied for guidance toward a decision.
  • When this tool is used to choose a plan, solution, or new product, results can be used to improve options. An option that ranks highly overall but has low scores on criteria A and B can be modified with ideas from options that score well on A and B. This combining and improving can be done for every option, and then the decision matrix used again to evaluate the new options.

https://asq.org/quality-resources/decision-matrix

Criteria Rating Form, Weighted Ranking


Use the criteria rating form when:

  • You have to select among several alternatives
  • You want to make a decision objectively
  • You want your group to agree on a decision

1 Start the session and list the alternatives available

 

2 Brainstorm decision criteria

You will be judging your alternatives against what you feel are the most important qualities each one should have. These qualities are called decision criteria. Brainstorming may be a useful way for a group to agree appropriate criteria.

 

3 Determine the relative importance of each criterion.

Rank the criteria and assign a relative importance (weight) to each. The total of the assigned weights should equal 100.

 

4 Establish a rating scale; rate the alternatives.

A suitable rating scale might be, for instance: 1= low, 10=high. each alternative should be weighed against each criterion, using the same scale for each.

 

5 Calculate the final score.

Multiply the weight for each alternative by the score and write this in brackets. Add up the numbers in brackets for each alternative and write the sums in the appropriate total boxes. Add any summary comments in the appropriate summary box.

 

6 Select the best alternative.

Select the alternative with the highest score. this alternative may not be the one ultimately chosen - if the group disagrees with the choice, they should review the weighting of the criteria and make the necessary changes. if necessary, repeat the process.

 

References

  • Chang, RY., and Niedzwiecki, ME., "Continuous Improvement Tools", Volume 1. 1993, 1995. Kogan Page Ltd. London
https://www.ifm.eng.cam.ac.uk/

Criteria rating form

Criteria rating forms help individuals and groups decide the best option or options among a group of options. In problem solving groups, they are often used in problem and solutions selection.

 

The criteria rating form can be used anytime there are criteria that will be used to inform the decision making process.  It is often used by interview teams when selecting a candidate for any position or when selecting new materials/textbooks. 

1. The criteria are selected by the group and a rating scale is defined.  In most cases a scale of 1 to 5 is used with 5 being the most desirable.

2. Weights are assigned to each criteria depending on its importance relative to the other criteria.

3. Each potential solution is also given a rating for each criterion, and the rating is multiplied by the weight of the criterion. 

4. The weighted ratings are totaled.  In this case, a 5 on the scale was most desirable so the solution with the highest total is judged the best option.  (I typically use the .5 to 2.0 rating scale.)

Remember that this is only a tool to collect data so be sure to discuss the final outcome before making the final decision, especially if the ratings are close.

 

Criteria Rating Solutions

There are a number of general criteria that can be considered when judging solutions.  What other criteria might you consider.  Criteria are personalized to each process.

        Control         Is the group in a position to implement the solution? 

                                                Effectiveness To what extend does the solution solve the problem?  (How likely is it to achieve the desired state?)

        Customer Satisfaction Will the solution result in increased satisfaction of parents, community members, staff, students, Central Office or others?

        Time            How long will it take to implement the solution?   (Some solutions          may take less time than others.)

        Cost of Quality To what extent will the solution reduce the cost of non-conformance?

        Cost            Are the financial resources available to support the initiative?            

        Acceptability Will those responsible for implementing the solution accept                             the solution? 

        

Criteria & Scale

Weighting

Potential Solutions

Brainstorm Criteria

 

.5 -`- 2.0

 

 

 

 

 

 

 

 

 

 

1  2  3  4  5

 

 

 

 

 

 

 

 

 

 

 

1  2  3  4  5

 

 

 

 

 

 

 

 

 

 

 

1  2  3  4  5

 

 

 

 

 

 

 

 

 

 

 

1  2  3  4  5

 

 

 

 

 

 

 

 

 

 

 

1  2  3  4  5

 

 

 

 

 

 

 

 

 

 

 

1  2  3  4  5

 

 

 

 

 

 

 

 

 

 

 

1  2  3  4  5

 

 

 

 

 

 

 

 

 

 

 

TOTAL

 

 

 

 

 

 

 

 Evaluating Alternatives:  Criteria Rating (Grid Analysis)

The Criteria Rating Matrix, or Grid Analysis, is a tool for objectively reviewing each of your solution options as related to the various criteria you need to consider in order to come to a decision about which solution to implement

For this analysis, list your options as rows on a table, and the criteria you need to consider as columns.  Each option is then rated by how well it satisfies each criterion

Criteria:

A

B

C

D

E

Total

Option 1

 

 

 

 

 

 

Option 2

 

 

 

 

 

 

Option 3

 

 

 

 

 

 

 Steps:

  1. List the alternatives you are considering.
  2. Brainstorm decision criteria (remember, identify all possibilities before critiquing them).
  3. Discuss the suggested criteria.

ü Is this criterion clear & unmistakable in its meaning?

ü Will this criterion be observable?

  1. Narrow the list of criteria to 3-6 criteria. 
  2. Establish a rating scale (e.g., 0-5 or 1-10).
  3. Rate each of the alternatives on each of the criteria.
  4. Calculate the final score.
Select the best alternative

Commonly Used Criteria

 

·       Ease of implementation

·       Cost

·       Ability to meet customer requirements

·       Equipment/resources required

·       Resource Availability

·       Lowest Risk

·       Fastest to implement

·       Long-term workability

·       Effective resource use

 

 

·       Impact on employee morale

·       Level of complexity

·       Human resources time required

·       Time required for implementation

·       Degree of control by the team

·       Political support

·       Disruption caused by change

·       Impact on the problem (high, medium, low)

·       Resistance of Stakeholders

 


Evaluating Alternatives:  Criteria Rating (Grid Analysis)

Example:

 A kayaking enthusiast, Patrick, is getting ready to buy a new car.  He needs one that will carry his kayak but will also be good for business travel.  He has always loved and wanted a convertible sports car.  So far, no one car he has looked at seems to fit all three criteria.

The vehicles Patrick is considering are an SUV/4x4, a comfortable 'family car', a station wagon, and a convertible sports car.  His decision criteria are cost, ability to carry a kayak safely, ability to store his equipment securely, comfort over long distances, attractive look, and fun.

Patrick draws up a table with the vehicle options and the decision criteria as shown below.  He then scores each option, 0-5, by how well it satisfies each criterion.  At this point he does not consider the relative weights of the factors

Criteria/Factors:

Cost

Kayak

Storage

Comfort

Fun

Look

Total

Sports Car

1

0

0

2

5

5

13

SUV/4x4

2

5

4

4

4

4

23

Family Car

3

2

1

4

0

0

10

Station Wagon

4

5

5

5

1

1

21


Based on this calculation, Patrick should buy the    ___________________________

Evaluating Alternatives:  Criteria Rating (Grid Analysis)

Weighted Criteria Rating

In many situations we also want to ask ourselves, “Are some of the criteria more important than others?”  Essentially, what you are exploring is the relative importance of each criterion.  For example, could it be that carrying the kayak and cost are more important to Patrick than comfort and look?

If that is the case, then you will want to weight the criteria, i.e. a weighted criteria rating matrix

Factors/ Criteria:

A

B

C

D

E

Total

WEIGHTS:

#

#

#

#

#

 

Option 1

Rating X weight

Rating X weight

Rating X weight

Rating X weight

Rating X weight

 

Option 2

Rating X weight

Rating X weight

Rating X weight

Rating X weight

Rating X weight

 

Option 3

Rating X weight

Rating X weight

Rating X weight

Rating X weight

Rating X weight

 

    Steps:

1.     List the alternatives you are considering.

2.     Brainstorm decision criteria (remember, identify all possibilities before critiquing them).

3.     Discuss the suggested criteria.

4.     Narrow the list of criteria to 3-6 criteria that will be most appropriate for identifying the best solution. 

5.     Determine the relative importance of the criteria & assign weights. 

6.     Establish a rating scale.

7.     Rate each of the alternatives on each of the criteria.

8.     Calculate the final score (remember to multiply the rating by the criterion’s weight).

9.     Select the best alternative.

       Back to our Example:

Patrick reflects on this decision a bit more and realizes that, in fact, the criteria are not equally important to him. So he decides to determine the relative weights for each of the criteria and recalculates using the ratings he previously identified

Factors:

Cost

Kayak

Storage

Comfort

Fun

Look

Total

Weights:

5

5

3

4

2

3

 

Sports Car

5

0

0

8

10

15

38

SUV/4x4

10

25

12

16

8

12

83

Family Car

15

10

3

16

0

0

44

Station Wagon

20

25

15

20

2

3

85

      

       Based on this calculation, Patrick should buy the    ___________________________.

The following is a sample of a 5 Point Numerical / Narrative Rating Scale including sample narrative ratings and definitions for each point value on the scale.

 

 

Scale

Rating

Definitions (Choose and/or Modify as Appropriate)

5 points (Pass)

Excellent.
Exceptional
Mastery.
Much more than acceptable.

Should ensure extremely effective performance.
Significantly above criteria for successful job performance.
Surpassed expectations.
Reserved for the exemplary set of skills that yield a particularly sophisticated approach to handling the situation.
Meets all major / essential / core criteria or acceptable equivalents and met three or more additional criteria.

4 points

(Pass)

Very Good.
Full Performance Behaviours.
Above average.

More than adequate for effective performance
Generally exceeds criteria relative to quality and quantity of behaviour required for successful job performance.
Meets all of the major / essential / core criteria or acceptable equivalents and several of the minor / addiitional criteria.
No major deficiencies exist in the areas assessed. Consistently demonstrated better than average level of performance.
Describes / demonstrates the full range of skills appropriate for handling the situation and the desired result, or outcome is obtained.

3 points

(Pass)

Good.
Acceptable.
Satisfactory
Average

Should be adequate for effective performance.
Meets criteria relative to quality and quantity of behaviour required for successful job performance.
Meets several of the major / essential / core criteria one or two of the minor / additional criteria or acceptable equivalents.
Describes / demonstrates a sufficient range of skills for handling the situation and the desired outcome is obtained.
Some of the major and minor criteria were met; some deficiencies exist in the areas assessed but none of major concern.

2 points

(Fail)

Weak.
Less than Acceptable

Insufficient for performance requirements.
Generally does not meet criteria relative to quality and quantity of behaviour required for successful job performance e.g. meets half or less of criteria.
Does not describe / demonstrate a sufficient range of skills appropriate for handling of the situation, or describes plausible but inappropriate behaviours for handling the situation or the desired result or outcome is not obtained.

0 – 1 point

(Fail)

Unacceptable.
Poor.
Much less than acceptable

Significantly below criteria required for successful job performance.
Few or no criteria met.
Many deficiencies.
A major problem exists.
No answer or inappropriate answer.

Describes/demonstrates counter-productive behaviours that have negative outcomes or consequences (make the situation worse).