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Data Based Lead Prioritization: Using Data, Statistics, and Causal Reasoning

Michael Griebe

(This is the third part of our three-part series about Data Based Lead Prioritization.)

There’s a lot to be gained by systematically using your data to prioritize your leads on a rolling basis. Let’s say you have 20 thousand prospects in your database and, after handling in-bound leads, your Sales Department makes outbound calls to 400 database leads a month. By deploying a statistical model to prioritize your leads, you are ensuring that your sales people are focusing on the top 2% available at any point in time. Better focus means less time wasted on low-probability prospects, no second guessing the right prospects to go after, reduced cognitive biases, and no “paralysis by analysis.” And as the available data grows, the prioritization improves.

A mathematically derived Lead Prioritization engine will enable your salespeople to:

  1. Focus their efforts on the best leads by sorting their leads in order from most likely to ‘succeed’ to least likely to ‘succeed.’
  2. The same algorithm that determines the priority can also highlight why a particular prospect is more likely to succeed than others. Knowing ‘why’ will help give your sales staff ideas on how to start the call as well as provides relevant insights.  
  3. And, in certain circumstances, you can estimate both the value of the lead and the   probability that it ‘succeeds’. Multiply these two numbers, and you get the expected value of calling the lead. Knowing this information can help motivate sales reps, create a business case for hiring sales reps, or provide the impetus to create new tactics.

You should know, however, that there isn’t an out-of-the box, configure-it-yourself, software-as-a-service Data Based Lead Prioritization tool. The data available to Sales and Marketing departments is always evolving. The sales process and marketing tools are different from one organization to the next. As a result, only a tailored solution will work.

There are five broad steps to building a lead prioritization engine: 1) Causal Modeling, 2) Data Collection, 3) Feature Extraction, 4) Statistical Modeling and 5) Systems Integration. In the next few sections we touch on each as we explain how to build a lead prioritization engine for your organization.

Causal Modeling

The first step is to create a Causal Model. You want to set a meeting between Sales and/or Marketing managers with the technical team (a data scientist, statistician, economist or mathematician) to discuss how sales usually unfold. If you have a dedicated trainer that handles CRM training, they are also helpful.  

The purpose of the meeting is to provide the technical team with the understanding necessary to build a causal model of the sales process. A causal model describes the sales process and relates that description to the data you have in the CRM. Here are some of the questions you may hear:

  • “How does a lead become disqualified?”
  • “What does this status mean?
  • “Does everybody try to sell everything?”
  • “Are all products available to all customers?”
  • “How do you handle multiple contacts at the same organization”
  • “When do you inform prospects of pricing?”

The causal model is used to determine the structure of the statistical model.  There’s a great book by Judea Pearl, The Book of Why, that describes more about Causal Modeling.

Data Collection

To build a Statistical Model, you need data. The data comes in two varieties: data that is unique to your company and data about your prospects.  

  1. You need data about your successes and failures – 500 of each is a good start. If you have less than 500 data points, building a statistical model is probably overkill.  
    1. ‘Success’ could be a ‘closed won’ sale, or setting an appointment, or converting the lead to an opportunity.
    2. ‘Failure’ could be a ‘closed lost’ opportunity, a failure to set an appointment, or disqualifying a lead. Looking only at your ‘sales won’, for instance, creates a survival bias that is hard to overcome with data.
    3. Other private contextual information about the success or failure – what marketing campaign led to the prospect, what rep handled it, what product or services were being sold, etc.
  2. With regards to prospects, you need a large group.  In this context, ‘large’ means that the salespeople would only be able to reach a small fraction of prospects in a month. For each prospect, you will need contact information and whatever contextual data you have acquired about them.

If you don’t have much information on prospects, the technical team can supplement your data with online information. For instance, you may find that you only have geo coordinates on a third of your prospects’ business, or you do not know the industry classifier for some of your prospects.  Maybe you have their websites, but you have not grabbed their keywords or crawled their content for clues about who they are.

‘Getting the data’ could mean connecting to an API and downloading data, purchasing data from a third party vendor, asking salespeople to categorize competing products or services, or collecting data off of public websites. In this context, you want to stick to data that can be obtained on-demand. In our experience, asking your sales staff to do more data collection on an ongoing basis does not work unless they already need the information to do their job.   

Feature Extraction

Not all the data you have is in the form it needs it to be in. Feature extraction is about taking data you have and converting it into a form best for modeling. For instance, 

  • corporate titles may need to be passed through a language processor in order to extract key words or groups of words in a title.  
  • If you decide to crawl the websites of your target prospects and capture the text regarding their products and services they offer, you will want to pass that information through a text mining algorithm, in order to reduce that information to a handful of salient characteristics.  
  • you may decide to take a list of industries (SIC or NAICS) and group them, or
  • take geo-coordinates and cluster them for modeling purposes.

All of these activities fall under the category of feature extraction.  

Statistical Modeling & Testing

Once the technical team understands the sales process, has collected the data, and extracted key features, they are ready to build a statistical model that will predict which prospects are most likely to succeed. Here are some things to keep in mind.

There’s no universally ‘best’ statistical model. There are a handful of very good statistical models that can be used to prioritize leads using data: logistic classifier, random forest classifier, neural net classifier or gradient boosted classifier are common examples. Which type will work best depends on the problem at hand, whether and how it needs to be interpreted, how much data you have, the expertise of the people producing the model and how you expect to deploy the results.  

It may take a few tries before the right statistical model is developed. After a model looks like it is performing well and difficult to improve upon, the technical staff will derive sample results (both in-sample and out-of-sample), some basic model documentation, and statistics about the model. The Sales and Marketing staff should consult the documentation, evaluate the results, ask questions and, if necessary, suggest changes to the model. Sometimes the question being answered is different from the question you need answered.  For instance, the model may answer the question “What is the likelihood that this prospect will become an opportunity?” And the question you need answered is “What is the likelihood that this prospect will become an opportunity within 2 weeks of a call?” A change like this has important implications for the model.

Expect to see back-testing results. After a final model is given a thumbs up, you should know how well the model would have worked had it been implemented in the past. A back-test compares model predictions with actual outcomes using past observations. Generally, you want your back-tests to be out-of-sample, meaning the model is built using most of the data and tested on the rest of the data. The out-of-sample test results should be similar to real world model results.

Ask for the confusion matrix. At the core of all these lead prioritization models is a classification model that classifies a lead as either “likely to succeed” or “not likely to succeed”.  The confusion matrix shows how often the classification model has been wrong and looks something like this:

Predict: SuccessPredict: Failure
Actual: Success1,000700
Actual: Failure80014,000

In the testing data, there were a total of 14,800 failures and 1,700 successes. This is a success rate of  ~ 1 in 10. The classifier is not perfect, however, it is still quite useful. If failure is predicted, 19 out of 20 times, failure follows. If success is predicted, 55% of the time success follows.

Even fantastic models are not perfect. The above model, with a 55% ‘success’ rate could be a fantastic improvement over the status quo. In this hypothetical, the success rate improvement over the random success rate is a healthy 5x (from 11% to 55%). In general, both false positives and false negatives are costly, and you can tune the model somewhat if you prefer one type of error over the other. 

Usually, this is enough information to make the decision to implement the model.  However, occasionally it is useful to do a beta-roll out to a subset of salespeople in order to fine-tune usability. This beta-roll out, in our opinion, should not be used to evaluate whether the model ‘works’, but instead, tease out the best way to use the model. Once you know that it would have worked in the past and it is usable, you’re ready to implement.

Pro-Tip: Don’t bother with a live test to evaluate if it works now. First, these tests almost always validate the original model’s results. Second, they are time consuming – you have to wait while the previous leads depart from the funnel before you get a clear signal on the impact of prioritization. And third, (if you separate salespeople into two groups to do the test), live testing can either create tension between the groups or the groups will share information, contaminating the results.

Instead, we suggest you integrate the model into the workflow and evaluate progress monthly or quarterly.

Integrating the Model Predictions into the Workflow

Once you have a tested and trusted statistical model, it’s time to put the predictions to use! Sometimes, if it is simple, the model can be directly coded into a CRM. Usually, however, the numbers are crunched outside of the CRM, and the results are put back into the CRM. This can be accomplished either with a scheduled importer or by creating RESTful API that the CRM can talk to. Regardless of the method, it usually makes sense to roll out the model in a two staged process.

Stage One – Manual Updates

For the first dozen updates (or first few months), it is helpful to have the technical staff directly engaged in creating the updates. Through trial and error, they may find that they need to make their system more flexible than they previously imagined. Or, after a few updates, the salespeople may have usability suggestions. Over time, however, these changes become far less common. At which point, having the technical staff manually run updates creates more cost and risk (they could be late) than reward.

Stage Two – Automatic Updates and/or creating an API

Once the updating process is mundane and predictable, the technical staff should automate model updates. Since they are no longer directly updating the system, they will need to create reports that ensure the program is continuing to work properly. These reports should:

  1. Check that new data is of comparable quality to previous data.
  2. Report on model health:
    1. Does the model continue to fit the data well?
    2. Do variables deemed to be statistically irrelevant continue to be irrelevant?
    3. Are recent model successful prediction rates in-line with past model results?
  3. Report on API usage statistics and uptime
  4. Compare before and after sales results.

With this in place, your sales team will always be pursuing the best possible leads! That’s it for our Data Based Lead Prioritization series. Happy Hunting! 

Data Based Lead Prioritization: Avoiding the Traps

Michael Griebe

(This is the second part of our three-part series about Data Based Lead Prioritization. The first part can be found here.)

Trap Number 2: Sending leads back to Marketing to Qualify – avoid the Pardot Pitfall (aka Ad-Hoc Lead Scoring).

To recap, we have too many leads. Now, the Sales manager hears the chatter by Sales Reps, sees the new problem of having too many leads, and tells marketing “Hey, we need marketing-qualified leads,” which may send marketing down the Ad-Hoc Lead Scoring rabbit-hole. Marketing, or maybe a resourceful data-miner succeeded in delivering to Sales all the leads they could ever want. But, instead of calling it a ‘victory’ and moving on to improve messaging, consolidate the brand, match messaging to personas, or increase awareness, they are tasked with the seemingly impossible: “get people on this list to prove to us that they are worth calling”.

“How do we do that?” Marketing asks.  “Make sure it fits our ‘ideal profile’ and then make them visit our website, then we will call them,” ($2-$20 per lead) or “make them watch a webinar, then we will call them,” ($5 to $100 per lead, plus production costs) or “make them open an email, register for an event, or fill out a form” etc. “then we call them”. 

There are a few problems here. First, every interaction Marketing secures with a prospect costs money.  It may seem cheaper than a Sales Rep’s time, but at $10-20 a click, maybe it’s not.  

Second, every time you ask a prospect to do more before your salesperson reaches out to them, you’re increasing the chance that the prospect will fail to see how your product or service fits their needs. Marketing, generally speaking, offers a broad path for most prospects. Sales offers a nuanced and adapted path that speaks to the customer’s specific problems. Putting more marketing interactions between the salesperson and the prospect may drive sales down instead of up -especially if it’s a complicated sale.

Third, when marketing sketches the ‘ideal profile’, they are probably committing the same biases as the sales staff. And, they may be driving salespeople to the same oversaturated market segment.

Fourth, after Marketing has agreed upon a profile, there’s the problem of assigning points to interactions. It seems straightforward. Someone visits your website, give them +3 points, for instance. Someone reads the second part of a three part series(!) +10 points. Someone requests a demo +15 points! It all looks like progress. There are two problems here:

  1. The obvious problem is that the point values are usually ad-hoc (i.e. guesses) to start with, and unless you have designed and deployed a thoughtful updating algorithm, they are going to stay that way. The point systems are rarely tested and optimized. But, even if they were by some third party, the points that worked for one company are unlikely to be optimized for another. 
  2. But, there is a bigger problem. Your point values might actually be facing the wrong way.  If marketing succeeds in getting people to interact with your marketing materials and they decide not to call you, it is likely they are somehow, subtly, not your target audience. All in all, the scores are going to be correlated with how much marketing has paid to get them to interact with your content.

The fifth and final problem with Pardot-Style Ad-Hoc lead scoring is that you can’t tell if it will work until months after you deploy. Out-of-sample testing or back-testing a statistical model, on the other hand, will tell you whether the program is worthwhile before deployment. Not only is it scary to deploy a program where the average outcome is unknown and the range of possible outcomes is limited only by your imagination, it’s also impossible for other departments to plan around it with confidence. 

Operations: “You have a new marketing initiative starting?”

Marketing: “Yes, we are implementing Pardot, it looks promising!”

Operations: “How much more inventory should I order?”

Marketing: “… ”

Statistical models allow for more clarity. 

Marketing: “If we had employed this model last quarter, our sales people would have converted 20% more leads to opportunities. But, the sales cycle is 6 weeks. So, we will need 10% more than we projected for next quarter, then 20% more for the next quarter.”

In the final part of our three part series on Lead Prioritization, we discuss how you can go about building a Lead Prioritization engine on-top of your CRM that’s accurate, up-to-date, and always improving.

Data Based Lead Prioritization: Searching Beyond Your ‘Ideal’ Customer

Michael Griebe

With marketing automation and advanced targeting capabilities, it’s possible for Marketing Departments to create leads faster than Sales Departments can handle them.  Maybe your Marketing Department, in a coup, has recently created thousands of leads at once. No doubt, having the physical address, email addresses, phone numbers, names, titles, NAICS or SIC classification, company size, property value, sales tax status and other metrics for a large slice of your B2B target audience is helpful – it’s worth doing.  Unfortunately, you may have overwhelmed your sales staff with options.

  • Do I go after large companies or medium sized companies?
  • Do I go after contacts with “manager” or “procurement” or both in their title?
  • Maybe I should go after companies in Lexington (suburb) instead of Boston (city)?
  • Maybe I should go after the webinar leads or maybe the conference leads?

You may be tempted to let your salespeople filter the leads and go after whichever ones fit their fancy.  But, if you have data, that’s a bad idea. In this three-part series, we explain the benefit of Data-Based Lead Prioritization.

In what follows, we set the stage with two common methods of prioritizing leads, 1) Letting Sales Reps decide the priority and 2) Letting Marketing qualify.  These two methods have some pitfalls, and we explain what they are. In the third and final part of the series, we discuss Data-Based Lead Prioritization – why it works and how it’s done.

The need for Data-Based lead prioritization is growing.  An abundance of data, Automated Marketing and Precision Targeting has made reaching and staying in front of a select group of people or businesses easier for everybody.  Unfortunately, ‘easier for everybody’ means it’s easier for your competitors too.  Any prospect that perfectly fits your ‘ideal customer’ profile is likely an obvious target for your competitors. By looking beyond your ‘ideal customer’, you can often find more prospects that are easier to reach and under-served. Unfortunately, human instincts won’t get you there.

‘Ideal Customers’ are great, but there are potential customers you may be missing out on.

Trap Number 1: Letting Sales Reps decide Prioritization for themselves.

To recap, we have too many leads and Sales is trying to figure out which to pursue. Individually, the sales reps will feel the urge to develop rules of thumb to help them decide who to call, or rely on experience to rule the day.  “Law firms definitely like our products” or “I’ve never been able to sell to an HR rep” are the types of rules of thumb that they develop from their own experience.  While based on their experience, these rules are usually incomplete or biased.

These rules of thumb are incomplete for four main reasons:

  1. An individual sales rep’s experience does not contain all of the organization’s relevant experience, only his or her unique set of experiences.
  2. Data that is less accessible is usually ignored.  For instance, if it is difficult to filter on a lead’s LinkedIn skills or the NAICS codes are not converted business relevant groupings, the sales rep may just ignore them.
  3. An individual will focus on the one or two variables that make the most sense to them, without understanding the entire context (attribute substitution bias).
  4. Or worse, they experience Paralysis by Analysis.

The Sales Reps, being 100% human, are biased for a variety of reasons:

  1. After they develop a rule, a sales rep does not systematically test outside of that rule -their objective is to maximize sales now, not to develop the best evolving rule (congruence bias).
  2. Talking among themselves, sales reps may eliminate entire groups of prospects because of the experience of one (bandwagon effect).
  3. A rep may have a streak of success, then over-rely on the information from that streak (clustering illusion).
  4. A rep is likely to over-emphasize current success over past success (present bias).
  5. If a rep has not seen a category or type of business before, they may avoid it altogether (ambiguity bias).
  6. A rep may overly rely on the first piece of information they receive about a customer (anchoring bias).
Plan your short and long-term prospect solutions

What can you do about it? We recommend building a statistical model of the prospect space and sales process so that your sales data can be used to tell you what leads should be pursued first.  But, that’s a long-term solution and if you are reading this, you may need something to start on now. Here are two short-term patches:

  1. Teach your salespeople and managers about cognitive biases. You don’t need a workshop or a psychologist on staff to introduce these concepts. Spending a few hours a quarter discussing cognitive biases and how they affect decision making may be enough to convince your Sales Reps to reach outside their comfort zone more. Their efforts won’t be optimized, but having the idea of ‘cognitive bias’ enter the workplace lingo will help.  
  2. You or your sales managers can comb through recent sales and regularly acknowledge the characteristics of any sale that are incongruent with common thinking about your ideal target.  “Hey guys, we just sold to an Engineering Consultancy’!  That’s not common!” Focusing on the odd or incongruent can help widen the net.

So, Trap Number 1 is asking the Sales Reps to set their own priorities. Usually, faced with too many leads, people will put their biases on overdrive in order to narrow-down the list and may end-up chasing the obvious prospects or leaving money on the table.

In the next part of our three part series, we look at Trap Number 2: Sending the leads back to marketing for qualification.

Marketing Absolutes: Practices that have stood the test of time that build great brands

Dirk Van Slyke

Note: This post is based on a “Lunch and Learn” presentation given by Dirk Van Slyke at The Cannon on March 6, 2019.

This discussion will address a pretty broad range of ideas, from branding to customer service to pricing. There are lots of opinions about these topics, so it’s important to understand the context from where they come. If you ask ten marketers, you might get hundreds of different tenets. Some may disagree with these. But there are some truths that I have found that have stood the test of time, including surviving and, yes, thriving during the monumental shifts in the marketplaces I have experienced in my thirty years of doing this. Also, these concepts will be pretty high level, as we could dive much deeper into virtually all of them.

Setting Context

First, let’s frame how we define “marketing”:

We consider marketing to be a high-level umbrella for not only the traditional four Ps (product, price, place and promotion), but also anything that touches your customer – that means sales, customer service, employee communications, events, etc.

Next, let’s define what we call a “brand.” My definition is this:

A brand is a relationship with your constituents that is either strengthened or weakened with every point of contact.

Each word is intentional – it’s a relationship because it starts with a common connection of some sort, it involves both parties bringing something to the table, it involves emotions and ultimately is heavily reliant on communication and expectations. Constituents is purposely vague, because it is anyone that has a stake in your brand, from owners and shareholders, to employees and customers. And the final segment references the impact of the give-and-take, and even ups and downs that relationships experience.

Some features we have found to be true of brands:

  • A brand can be anything. Yes, it is a company, or organization, even a cause. But it’s also people – of course, Michael Jordan was one of the first to truly monetize a personal brand on a global and multi-billion dollar scale. But today, we see multi-million dollar brands from online channels that encompass nothing more than watching people do things, like playing video games, garnering hours and hours of attention each day that far surpass even the heyday of broadcast television, back when there was only a handful of channels.
  • A brand is what your customers say you are, not what you say you are. In this age of transparency, the number of exposures you control of your brand are exponentially outweighed by what is going on in the outside world.

Okay, so let’s dive into the 21 Marketing Absolutes: Practices that have stood the test of time that build great brands.

  1. Growing and scaling are not the same thing. Growing is making improvements in your key areas (revenue, customers, profit, etc.). Scaling is preparing your infrastructure to handle explosive growth.
  2. Do your homework on your name – it’s the hardest to change later. By homework, we mean hire a great attorney. It’s difficult to recover from a name change – positive reviews, SEO authority, marketing momentum, brand equity, etc. all become sunk costs. Also, your name should be unique, descriptive, or at least have a great tagline that describes what you do and differentiates you from the competition. There is a lot of clutter out there, and you need to break through quickly.
  3. To be believable, be authentic. In marketing, we certainly can “take liberties with the language” to sell our wares, but there is no faster way to lose trust than to make a claim that does not meet your customer’s expectations or that you cannot defend. Know who you are and stick to it. And don’t try to be all things to all people. Also, avoid overused phrases and clichés in your marketing like “value” which have lost their true meaning – show them, don’t tell them.
  4. Great positioning is the intersection of a great product offering, something your customers need, and something that your competitors do not offer.  It sounds simple, but it’s really hard to pull off. But this is how you stand out, and you will win in the long run, regardless of any other circumstances – be great and great things will happen. Lots of organizations say this, but very few have the courage to show up and do it every day and make the tough decisions to stand the test of time.
  5. Really understand your customer, and their expectations.  People’s expectations are pretty low these days – continuously exceed them. The best way to live this is to practice empathy. Get into the mindset of your customers – whether it’s a negotiation, a new business pitch, whatever. Context matters – you need to align your messaging with the mindset of the environment in which your message will be viewed, what they are experiencing, etc.
  6. Complaints and mistakes are opportunities, not to be dismissed. There are simple rules to follow, which can turn a detractor into an evangelist in a matter of minutes.
    1. Hear them out and acknowledge the error. And give them the benefit of the doubt.
    2. Apologize – sincerely. You’re wasting your time making excuses or coming up with “reasons way”. Blaming others gets you nowhere – they don’t care that your internet provider had an issue. Just own it.
    3. Make it right – there is a lot of leeway here, but at a minimum, make them whole again.
    4. Make sure it doesn’t happen again. This is the most difficult to pull off in the long run. Use these events as feedback for continuous improvement. And look at the data – a few here and there are not worth overreacting to, but consistency indicates a bigger problem that should be addressed. Realistically, things will always go wrong. Nobody expects a brand, or an individual for that matter, to be perfect. It’s how you deal with the mistakes that separates the mediocre brands from the great ones. Here are a few other guidelines to help:
  • Act fast and you can avoid this happening publicly.
  • Be transparent – the environment we operate in already is.
  • Don’t hide negative reviews – respond publicly so people can see how you handled the situation. If you bury them, you decrease your credibility. Only allowing 5-star reviews is not believable.
  1. Be a giver, not a taker. This is true of your personal career and the brands you work on. If all you are doing is selling, you will lose, and people will avoid you. Be generous with your time, talents and assets and you will make people want to work with you.
  2. To be trusted in the long run, do the right thing. Always. In this age of transparency, you will be discovered for how you behave. Honesty, integrity and transparency are better than mistakes uncovered by customers or third parties. In the absence of information, people fill in the blanks, and often will assume the worst. Furthermore, your competition is always watching, looking for ammunition.
  3. Treat marketing as an investment, not an expense. Like any investment, you can mitigate your risk, diversify your investment, etc. As long as you have the right tracking in place, and the appropriate analytical resources to truly tease apart what works and what doesn’t, you are being a good steward of your resources.

Also, you get what you pay for with your goods and service providers – whatever it is, materials for your production, a marketing firm, etc. The best is rarely the cheapest, and actually end up saving you money in the long run. Hire the cheapest, and you may have to redo it over and over, and the work, and its impact on your brand, will never make you a leader.

  1. Test small and go big. This is one of our core methodologies, and why we currently employ advanced mathematical capabilities. With all the information, resources, tools and analytics available to us today, this can be done very inexpensively. You can grow and scale with confidence using real-world, live experiments that are much more reliable than even the finest primary research.

For this reason, we also believe in a real-time budgeting process. That means establishing a process for spending wisely, trying lots of things and ensuring they are always at, near or approaching profitability. You will no longer have to anguish over the appropriate percentage of spend, or a firm number that limits your imagination or opportunities that come along unexpectedly. Every organization struggles with having more opportunities than they have the resources to pursue. This is a great way to help you prioritize those on a regular basis.

And remember, the true ROI of any activity should be based on the long-term customer value, not just the single first instance. Once you acquire a customer, and continually exceed their expectations, your dependence on new customer acquisition will diminish over time.  

  1. Invest in a downturn. This is a tough one.It takes courage to invest in a recession. Almost always, your competitors will retract during tough times. They will stop building their brand, cut corners on quality and they will let go of really good people. If you continue to invest, you will show your stability and resilience in tough times. Typically, you can negotiate better pricing for your materials and services, and you can find great people. You can still grow during this time, and when the recession is over, you will be the first to benefit.
  2. There is no silver bullet. It takes multiple strategies and tactics, executed consistently over an extended period of time to build a successful marketing program and grow a great brand over the long run. Celebrate big wins, but don’t get complacent. They can disappear overnight.
  3. Don’t follow your competition, lead your industry! Industries that do this result in parity and price wars. Always be looking forward and let them chase you.
  4. All web buyers are researchers and price shoppers – tread carefully on pricing. Advancements in technology have made comparing prices online immediate and effortless. The cost of research is low – you will be compared to your competition. So there will be a tradeoff to the brand impact in terms how low you are willing to go to earn the business, and how much of it you can handle at that price. You can always lower your price, but it is really difficult to increase it.
  5. Discounting is a short-term solution, with long-term implications. If used too often, you train your customers to wait for sales. There is no better example of this than Jos. A Bank Clothiers. SNL takes Jos. A Bank’s famous sales to an absurd and hilarious level and the brand is still trying to re-train their customer base years later. https://www.nbc.com/saturday-night-live/video/jos-a-bank-cleaning-product/2768588
  6. Superior execution trumps a great idea. There are many examples of good ideas gone bad; Segway started out as a mass item, but completely misread the pricing cues, as well as city infrastructures, storage and transport issues. Lots of examples exist of decent ideas well executed, such as Amazon and Uber. This is true of your marketing execution as well – I’ve seen many examples of really good creative ideas that fall way short due to budget-conscious clients.
  7. Don’t wait for perfection. Get to market fast, don’t wait for it to be perfect. You can launch in a controlled way, set proper expectations by being authentic and transparent, gain market acceptance and continuously improve. Focusing on customer satisfaction will rule the day. The best validation of your product is when a customer spends money on it.  
  8. Question everything. There should be a reason for everything you do – especially in marketing. Be objective – subjectivity has no place in marketing these days. Use empathy with all your important constituencies – what would your customer think? What would your board or investors think? And “We’ve always done it that way” is the worst reason to do something, behind “no reason at all”.
  9. Only consider changes in your organization that are good for your customer. CFOs rarely make customer-driven recommendations. By going with a cheaper ingredient or supplier, you may be able to save millions of dollars per year. However, the damage you do to customer loyalty is forever. No greater example of this exists than CompUSA – among the many poor decisions that led to its demise, none are more memorable than the great idea one of their leaders had to get rid of all their senior level, highly experienced store associates, to replace them with younger, less expensive employees. They saved money at first, but customers no longer had a reason to trust them.
  10. Anyone can be an “expert,” many are not. You don’t need a certification to be an expert – and real-world experiments are the best teacher – remember test small, go big!Don’t assume you can’t be better than those that do it every day – in fact, many don’t keep up with the latest developments.  As fast as the market moves and changes these days, it’s easy to get in a rut.Learning should not only be a constant in your career development, but think about it as a real-time, daily practice.
  11. Anyone can be “creative”. Your brain is like a computer – identify the problem, do the research, then leave time to process.While at rest is the best time for it to provide the solution – rote activities that don’t take a lot of brain power.I make sure I capture ideas when they come to me – in the car, in the shower and when I wake up in the morning are the most common times of breakthrough and clarity.This works for creative ideas, or any problem-solving.By the way, procrastination can be good because it leaves more time to process. Another good practice is mixing up the neural pathways in your brain, forcing it to learn new things, like taking a different path to work. It scrambles things up in a good way and reignites your brain.

I’d love to hear your thoughts and reactions – good, bad, or indifferent.

Reviews: Your Most Precious Resource

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In today’s age of connectivity, it is easier than ever for people to share their thoughts and experiences. “Word of mouse” is the new standard, as people are taking to social networks like Facebook and Twitter to make their voice heard, rather than the more “traditional” way (you know – like actually talking to people). Interactions are now less personal and more public. These platforms, and countless others have dramatically increased the rate at which information spreads. While this has many implications on the business landscape as a whole, the aspect that we will focus on during this article is the power of online reviews.

Using reviews to develop your product

One of the most powerful characteristics of a product or service review is insight of how your product is performing out in the wild. By the wild, we mean that the user experience is no longer under control. Rather, the experience is bound strictly to the product’s design and the customer’s understanding of it. This is important because it speaks to its ease-of-use and overall quality. A common example of this is testing and reviewing products in beta mode in order to get feedback before taking a product to market.

Positive and negative feedback unveil contrasting viewpoints of your product, and thus different takeaways may be derived from each. Take each seriously, as both are gold mines for improvement. Negative feedback is most effective in helping to pinpoint flaws or weaknesses in your product. This is important because it allows the development team to make product adjustments and alleviate the issue before more users encounter the same problem. The foundation of a thriving business is consistent improvement, so this process will likely be repeated continuously over your company’s life span.

Positive reviews, on the other hand, reaffirm what you are doing correctly. More importantly, they provide a satisfied customer that would likely to be willing to give further feedback (especially if given incentive). What better way to improve your product than to take suggestions from people already using it? This gives you the opportunity to ask deeper-diving questions, like “What are some features you would like to see added to our product?”. This type of feedback is invaluable as it may spawn product ideas that you yourself would never have thought of. It may also reveal aspects of you target audience’s customer journey that you were not previously aware of.

Using reviews to market your product

Despite popular belief, negative reviews may actually hold more marketing value than positive ones. Simply put, negative feedback is something that every business professional has or will experience at some point in their career. However, the manner in which you respond says more about your business than the feedback itself. The most important thing to keep in mind is that the customer is always right. If their poor experience is due to their own wrongdoing (misuse of the product, etc.), it is your responsibility to walk them through what went wrong and how they can have a better experience the next time – without placing blame. If, on the other hand, the poor experience is the company’s fault, the situation should be handled differently – refunds and discounts are some more common forms of compensation. Rather than placing judgment, future customers will feel more comfortable with your brand as they know that potential hiccups will be well taken care of by customer service.

Utilizing positive reviews also plays a great role in marketing and brand-building. While posting positive reviews as anecdotes is a common marketing technique, letting reviews speak for themselves is often the most effective method in building trust amongst your target audience. Although there are tools and methods that can be leveraged to drive reviews, offering a great product is always the best rule of thumb – along with continually exceeding your customers’ expectations. Reviews will likely build slowly in the early stages of a company, as this is the nature of business. Due to the network effect, reviews will grow exponentially over time; tending to reviews early on in your business’ life cycle will help establish a positive trajectory for future customers to feed off of.

Once your company has built up a common trend of positive reviews and great customer service, you have essentially built up a marketing platform that will cost you absolutely nothing. Furthermore, this marketing “campaign” of sorts says more about your company than any advertisement ever could, because it involves people’s real life experiences with your product. This network of reviews will serve as many of your customer’s reference point – if potential customers are looking to justify their purchase or compare you with the competition, the quality of reviews may likely serve as a differentiator in their purchasing decision.

 

The underlying message in this post should be clear: Reviews are an extremely valuable asset to any company looking to improve. Hopefully this has provided some understanding of how to properly leverage your reviews, both negative and positive. It is important to keep in mind that the value of your reviews is directly tied to the action you take based on these insights. If you are a business owner and have not been utilizing your feedback to your advantage, we strongly suggest you begin today – it could make all the difference in the way your company is viewed.

Why it Pays to Spend More in a Recession

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Many people blame economic recessionary times for the failure of their business. But, what if you could flip the negative features of a recession into a marketing edge? Despite the name, economic downturns have both up and down-sides. If you let a recession dictate your actions, odds are you will come out on the losing end. However, leveraging a downturn to your advantage is a completely different story.

Get great deals on business services.

During a recession, the consumer is not the only party that becomes more frugal. Companies, as well, tend to spend less on luxuries. One of these “luxuries” that they hold back spending on is marketing. However, this is a HUGE mistake. Basic economics will tell you that a lesser demand for marketing services yields reduced prices, making it cheaper to invest in your brand. In an economy where everyone is pinching pennies, now is the perfect time to steal a great deal on a website refresh, SEO services, or any other capabilities your business is in need of. This also presents a great opportunity to get a bargain on paid media, as holding companies are more willing to negotiate price. This reduced demand and pricing will bounce back when the market rebounds, so take advantage of the opportunity while you can.

The reason so many business owners see marketing as a luxury rather than a necessity is because they are accustomed to immediate bottom-line results in other divisions of their business. However, this is rarely the case with marketing. The underlying goal of marketing is to tell a story about your business or brand that resonates with the people you want to hear it. As your business evolves, so will the story behind it. For this reason, not only is marketing a long-term process, but a process that will follow your business to its grave – ever seen those “going out of business” sales?

Marketing during tough times indicates a strong brand and instills confidence in your target audience’s perception of your business.

During trying times, consumers become more sensitive – both emotionally and financially. This means that marketers need to deploy new strategies to appeal to this change in consumer outlook. However, many business owners resist change under the assumption that “If it worked before it will work again”. According to a Nielsen study conducted in 2011, “Ads pitching value saw a significant lift in effectiveness during the recession.” This indicates that during recessions, consumers need a brand that they can rely on to get the most value out of every time. It is of every businesses’ best interest to take on this role in order to build trust and eventually brand loyalty.  While you will not receive the immediate patronage of every potential customer, staying top of mind is much easier when your competitors are retreating. This increases the likelihood that they will purchase your product when times are right.

 

Henry Ford once said, “A man who stops advertising to save money is like a man who stops a clock to save time”, and this is coming from the man who revolutionized the supply chain. In today’s competitive digital age, it is more important than ever to market your business – It’s pretty difficult to monetize an incredible product if nobody knows about it. In order to build a strong brand, you must be willing to bet on yourself. Take a long-term approach – have faith in the economy, but most importantly, have faith in your brand.

New Age Marketing and How to Make the Most of it

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Since the internet and computer have taken over our day-to-day lives, it seems that there is more information on our hands than we know what to do with. With the development of search engines and social media as vehicles for advertising, marketers and researchers have an eye on nearly every aspect of the customer journey. While this may be overwhelming to the untrained eye, getting this information in the hands of someone who can make sense of it all will make all the difference.

One of the biggest advantages of search engine and social media marketing over traditional methods (television, print, etc.) is that advertising has become extremely targeted and customizable. Facebook currently offers hundreds of different factors to target, allowing you to deploy different ads based on these factors. These range from less personable demographics like geographic location and language spoken to very personal factors like traveling habits or even food and beverage preferences. While many of these categories may seem like white noise, finding the factors that have implications for your business will increase the chances that advertisements are being seen by your target audience.

Along with new methods to target your audience has come advanced advertising performance metrics. Some of the more popular metrics that people keep a close eye on are things like bounce rate, impressions, and conversion rate. Bounce rate, for example, is the percentage of people who visit your site and leave immediately without taking any action. This is an important metric because it gives you a gauge of your users’ experience with your web page. A high bounce rate likely means that there are aspects of your home page that need to be optimized, or that your targeting needs to be refined to a more accurate representation of your target audience.

So, what does all of this extra information mean for your business? Well, first off, you need somebody who understands all of it. This doesn’t necessarily mean that the CEO needs to take an SEO crash course, however, there has to be someone within the organization that is following the progress of ongoing campaigns and is able to translate the results to upper management. There are many minor details regarding the rules, methods, and metrics that can only be fully understood by somebody who has been working with online campaigns consistently for an extended period of time. Having somebody who can take meaningful insight from this seemingly daunting platform adds an entire new dimension to your marketing department’s decision making.

An employee who understands the nuances of online marketing also further potentiates other departments of your organization. Having your online marketing guru and finance department collaborate could bring to light some major room for improvement; a great example of this is the potential to drive higher margins.  Using some commonplace analytical tools like R and PowerBI, analysts can help advise their marketing department on things like ROI, optimal advertising spend, and conducting attribution analysis. Furthermore, somebody with a deep understanding of both marketing and analytical principles can identify patterns in customers’ buying patterns to optimize targeting techniques.

The age of digital marketing is upon us. Whether you are B2B or B2C, it is inevitable that online marketing can have an impact on your bottom line.