Tag Archives: growth

Loyalty Marketing Part II: Making a Program and Keeping It Successful

Read part 1 of of my series on loyalty marketing.

In my previous post on loyalty marketing, I talked about the different types of loyalty programs, and how to identify which type of program your company should pursue. Once that happens, do you slap up generic version of a program that tackles your needs and call it a day? Absolutely not. Now that you’ve identified a program type to target, you need to determine a version that your users will respond, that will fit your brand, is profitable over the long term, and is future proof. Let’s tackle user response first.

Understand Reasons Why
Your can’t expect your users to change their behavior until you understand why their behavior is the way it is. Let’s say most of your users use your product regularly, but not every time they have the problem you solve. In order to create a successful program, you need to figure out why they don’t use you the times they don’t. The only way to do that is to talk to them. Take random people in the segment you’re trying to change and arrange a phone call. Reward them for it. In 20 minutes, with targeted questions, you can learn all you need to know about the time they’re not using you. A standard question to learn this is “Tell me about the last time you did X and didn’t use us.” Keep doing these calls until you start hearing the same types of responses over and over. In my experience, things settle around four or five reasons. For loyal, but infrequent users, it works very much the same. Talk to users, but this time ask “Why don’t you use X for [new use case]?”

Understand the People Behind the Reasons, and Pick a Reason
These phone calls don’t give you any statistical representation around how popular these reasons are for the broader audience that isn’t using you every time. So, now that you have your reasons, you can survey the broader group, asking them, “When you do X and don’t use Y, which reason best describes why?” and make the answer multiple choice with the responses you received over the phone. With a good enough response, you can now stack rank the reasons why people aren’t loyal to you. Some may be product changes you need to make. Some may not be helped. But, more than likely, you can address most of them with an incentive. You can go further down the phone calls + survey rabbit hole until you have full personas of users. Knowing the reasons why users aren’t loyal and what types of users you have can make you say, “I want to target this reason for this persona.” The same philosophy applies to incentivizing use cases. Our survey question is the same question you ask over the phone, except now it’s multiple choice. The goal again is to be able to say “I want to target this use case for this persona.”

Testing the Program
Now, you’re ready to build a program. At this point, it’s mostly a creative exercise leveraging psychology. Invent a bunch of a programs that might incentive these users, narrow down the ones that are most likely to incentivize users and be profitable, and test. Email is a great way to test different programs because you don’t have to build much and can book keep manually to get enough data without users knowing it’s not a real thing. It is also not a bad idea to run your users through these program ideas over the phone or in person, but remember that what they say and what they’ll do may be very different. Still, talking to them can prevent some gotchas.

Once you have a program, you need to test in a live way. Depending on what type of program you build, you may be constricted. For example, Yummy Rummy at GrubHub was considered a sweepstakes, so we could not legally have a control group. A control group is always the best way to test. Sweepstakes laws are at the state level in the U.S., so if you have two states that perform very similarly, that may work. If you don’t, you need to measure pre and post data. Pre and post data is not ideal for a few reasons. The main one is that loyalty programs typically take time to change behavior, and if you turn them off, it will take time for behavior to change in reaction to that as well. You don’t want to be running the program for a over a year, and not be sure if your pre data is still relevant. What typically happens in these scenarios is that programs are pulsed, like the McDonald’s Monopoly game being available for a limited time yearly. There is too much money being spent on a loyalty program typically to not know for sure if it is working or not.

Long Term Success
One other dirty little secret about loyalty programs is that they tend to ebb in effectiveness over time. Humans are motivated by variable rewards, and if your program is static, your users may become used to it, and it may not create long-term behavior change. That is why I recommend creating a variable program. At GrubHub, we made Yummy Rummy available every three orders instead of every order, and the reward could be anything from a free drink to free food for a year. Furthermore, if you lost, you got a consolation prize that was something random from the internet. But, I don’t think that is even enough. You should strive to think of your program as constantly evolving to stay interesting to your users. This will make your program stay effective for longer as well as give you the flexibility to tweak elements to make it more interesting to you as the business. I have seen many companies stuck with a program they no longer think is effective, but too afraid to shelve it because of potential user backlash.

The other advantage of creating a living, evolving program is that, if the original incarnation is effective, you can change it to move users further up your user lifecycle. For example, let’s say you’re trying to incentivize platform use in your original incarnation of your program. You might be very successful at that, and then find an opportunity to take those same users and get them to use the platform more by incentivizing use cases. Now, you can do that by evolving the same program instead of starting from scratch. Or, you might have taken loyal users and gotten them to use you for more use cases. Now, you can adapt that same program to build a moat around them. This all boils down to what a holistic loyalty program should look like in three steps for most internet businesses:
1) Build loyal users in one use case
2) Increase frequency by incentivizing use cases
3) Build moat around those users

This happens to be how most marketplaces or social networks grow into behemoths. They nail an initial use case, build a loyal user base for that, gradually expand use cases, and then work to keep those users locked into their platform.

Loyalty Marketing Part I: Strategies and Segments

There seems to be a lot of confusion about loyalty marketing and how loyalty programs work. To an outside consumer, I guess the confusion is understandable. Most loyalty programs are branded as a value to the customer, a reward for their dedication. Most loyalty programs’ primary goals are not to add more value to consumers (though when they’re done well they do that too); their goal is to create more value for the company. I’ll break down how to think about loyalty if you are a business that is wondering if a loyalty program makes sense for you.

The first thing to understand is that every business has a loyalty problem; it just might not be the loyalty problem they’re expecting. To make this clearer, I’ll split consumers into four areas. Depending on where most of your company’s consumers fit is where you’ll spend your effort in thinking of a loyalty programs. The first thing to do is split all of your consumers into loyal and non-loyal and frequent and non-frequent. Loyal is defined by doesn’t use a close competitor as well as you for what your product/service does. Frequency is a bit more nuanced. Your product/service should have a target frequency you’re setting. For Pinterest, that might be daily. For GrubHub, that might be once or twice a week. You then can build a 2×2 matrix like the one below.

Each of these four buckets requires a loyalty program targeting different actions by consumers. Just to be absolutely clear, let’s go through that exercise for each segment.

Frequent, Loyal
Action: Keep consumers doing what they’re currently doing

Frequent, Non-Loyal
Action: Get consumers to migrate usage of competitors to you

Infrequent, Loyal
Action: Get consumers to use product/service more

Infrequent, Non-Loyal
Action: Determine if product issue can increase frequency. If not, ignore.

Now, we should talk about these strategies in a bit more detail. I’ll skip infrequent, non-loyal since it’s a combination of two other strategies, and probably implies a product or market problem.

Infrequent, Loyal Strategy: Incentivize Use Cases
In this segment, consumers use your product loyally, but not enough to your liking. This implies that there are not enough use cases for your product in the eyes of the consumer. That could be because these use cases do not exist, or because the consumer doesn’t perceive them to be relevant. If the use cases do not exist in your product/service, you need to build them into your product. Take, for example, Homejoy expanding into all sorts of home services after starting with house cleaning. If the use cases do exist in your product/service, but consumers aren’t using them, you need to invest in awareness or incentivizing a trial of them. For Pinterest, this might be upselling someone who uses the service for recipes to try planning a vacation with the service, or a web Pinner to try the mobile app. For GrubHub, this might be giving a discount for a pizza orderer to order sushi, or a web orderer to try their first mobile order, or a delivery user to try their first pickup order.

These opportunities might not exist or be worth the effort. When I worked at Apartments.com, we knew people would only look for apartments once a year or less. There was not much we could do influence that. What we could do was stretch our product to be useful for not just the apartment search, but also services you need once you find an apartment e.g. moving. Beyond that, there wasn’t much opportunity we could tackle, meaning we’d probably have to spend money to acquire those same users whenever they looked for an apartment again.

Frequent, Non-Loyal Strategy: Incentivize Platform Use
In this segment, consumers are very active, but don’t always use your product/service over a competitor. This is the most common type of loyalty segment because it’s easy to understand the upside. You can typically measure how much activity occurs off your platform. Here, you need to invest in an incentive to move those uses onto your platform. This typically takes the form or rewards points or punch cards.

Frequent, Loyal Strategy: Build Moat
In this segment, consumer are very active and don’t use anyone else for your product/service. These are your best customers. So, a loyalty strategy shifts from trying to increase how often someone use the platform to doing all you can to make sure these consumers don’t decrease their use of your platform or are wooed away by a competitor. This is by definition a money losing strategy to decrease risk instead of a money making strategy in the first two segments. Moat strategies can take many different forms and are frequently misunderstood. Some start out looking like the same strategy as frequent, non-loyal. One common one is to increase switching costs. One example of that is Facebook shutting off friend access to competing apps.

Many other moat building strategies get much more creative. They rely on looking at every possible risk to your consumers doing less of what they’re doing today and trying to address it. One of the most ambitious is Google’s launch of Android. Google makes most of its money from web advertising. They saw consumer attention shifting from an open, web platform they increasingly controlled via their browser Chrome to closed platforms on mobile owned by competitors Microsoft and Apple. So, they acquired and put hundreds of millions of dollars behind their own, open operating system in Android, which they charge no money for, but powers most smartphones all over the world. This is all so they could continue to control how people searched and saw ads in a mobile world.

So, we can go back to that 2×2 with our strategies now.

Now that you understand the segments and their corresponding strategies, you need to identify where the opportunity is for your product or service. The easiest way to do that is to run a survey to determine loyalty, and mine your user data for frequency. Then, see where the highest percentage of your users are.

This post covered how to identify which segment to focus on and the appropriate strategy to pursue. My next post will talk about making that strategy and implementation successful.

Read part 2 of of my series on loyalty marketing.

Distribution Real Estate, or an Untalked About Element of Facebook’s Acquisition and Unbundling Strategy

Many have opined about Facebook’s two major strategies over the last couple of years. The first is their aggressive stance on acquisitions, a 180 from their acquihire only approach pre-IPO. This includes Instagram, WhatsApp, Moves, and the real outlier Oculus. The second and more recent is their unbundling of core functionality from the main Facebook app, as with a standalone Messenger app, a standalone Camera app (now being replaced by Instagram), a standalone ephemeral messaing app (Poke and now soon Slingshot), and a standalone launcher app (the miserable failure Facebook Home). One might think I am against such a strategy due to my previous post criticizing unbundling. What Facebook is attempting is something different though.

It is rare that companies get to a point where they start to pursue a moat strategy. A moat strategy makes sense when a core business is so profitable that the most sensible course of action is to build things around it to protect it instead of trying to increase value of customers or try to attract more customers. Google is a famous example of this with Adwords. Its core business is so successful that it pursues projects like free mobile operating system Android solely focused on protecting distribution of Adwords as the world shifted to mobile, and free web browser Chrome to protect distribution of Adwords on web as Microsoft tried to bundle browser (Internet Explorer) and search (Bing) together.

While an entire company ascending so far to the top that its main course of action is building a moat is rare; it is more common on specific distribution channels. Take, for example, search engine marketing and search engine optimization. Once you ascend to the #1 result for an organic search, a normal marketer might stop there and try to optimize other channels. But savvy search engine marketers are just getting started then. A savvy search marketer knows that a #1 result organically does not guarantee that all clicks go to one’s own domain. There are still nine other organic spots and the paid ads, which may still appear above your result. So hardcore search marketers start working on their next trick, owning more than one spot on the search result.

I pursued this strategy at Homefinder. We would take the #1 ranking for certain keywords like “Fayetteville homes for sale”. Once we achieved this, we would bid highly on Adwords as well to take a second spot on the page. Then, we would work with affiliate marketers to bid below us in Adwords to take the second, third, fourth, etc. spots on the page. If someone clicked on those listings, they would go to a different website (as Google only allows one paid ad per domain). But when someone searched on those domains, they were redirected to Homefinder. By pursuing this strategy, we could own perhaps five or so spots on Google for one search, making the likelihood of someone landing on Homefinder much higher than if we just had the #1 spot and nothing else.

The travel companies like Expedia are the masters of this, with multiple brands ranking on the top of Google that are all owned by the same conglomerate not very differentiated. Almost all travel purchases start with Google, so the travel companies maximize this distribution channel more than any other category. For example, Expedia has Hotwire, Hotels.com, and Trivago. Priceline has Booking.com, Kayak, and Agoda.

You might ask what this has to do with what Facebook is doing. Well, on mobile, Google is actually not the only search engine of note. The App Store and Google Play are just as important to optimize for. App store rankings are largely determined by recent download counts, but Google Play is a bit more sophisticated with many engagement metrics. Facebook currently ranks #1 for free apps on Google Play. Did Facebook stop there? No, it unbundled it’s Messenger app completely, which is now the #5 free app. It purchased Instagram, which is the #4 free app. It tried to buy Snapchat, which is the #6 free app. And it purchased WhatsApp, which is the #14 free app. When you already own the top result, you tried to grab as much of the rest of the top real estate as you can.

If Facebooked buys Pandora in the future, don’t be so shocked. This is not just true on the app search engines though. The home screen of the mobile phone is another limited piece of real estate that is incredibly important. Facebook is likely on the home screen of more mobile users than any other app. But it is still only one app among many one can click on when they pick up their phone. So Facebook is focusing on other app that already have wide home screen distribution. I had Facebook, Instagram, and Moves on my home screen. That means, Facebook had a 3/16 chance every time I opened my phone that they would receive some sort of engagement from me.

When you’re optimizing channels, I encourage you to think about the real estate you own on each one, and how you can own more. It’s an incredibly effective strategy even if you’re not quite at the moat building stage.

With Growth, Don’t Forget About the Long Term

Growth is an area for startups that is traditionally aggressively short-term focused. Development cycles are quick, results are measured in experiments as soon as things go out, and we react to all this data. Because of the nature of most startups, most marketing or growth tactics focus on acquisition as well. Getting more people in the door is almost every startup’s biggest issue.

This creates some challenges to doing long-term effective growth. I’ll highlight a few examples I’ve run into, and one tip on evaluating the more nuanced balance between short and long term growth.

At GrubHub, we added a feature for someone to sign up as a guest and order very early on as a company. The reasoning for this was simple. The easier we made it for someone to check out (or the easier it was perceived), the more likely an order would be completed and we’d make money. People visiting GrubHub are hungry, and hunger and patience don’t typically co-exist well.

Way off into the future, we started noticing our one month repeat purchase rate for new diners started to sink below our previous year’s levels. This had never happened before. We’d also continued to growth our repeat purchase rates year over year. Obviously, we were bringing in more new diners, so we thought it could be a natural decline in quality as you target less early adopters and a bigger base. So, the first thing we looked at was our repeat purchase rates by referral source. Most of our referral sources were down, so it wasn’t one new channel that was causing this.

Then, we cut the data by guest vs. Facebook vs. account creations. Over half of our new diners joined as guests. When looking at the data this way, it was clear that guest repeat purchase rate was declining rapidly, but the other types of accounts were doing well with driving repeat purchasers. So, we faced a decision. Do we remove the ability to order as a guest and potentially lose half of our new diners?

We decided to test redesigning the signup page, amplifying the value of creating an account, and hiding the guest option at the very bottom with a link that said “I don’t like convenience. Sign me in as a guest.” We monitored overall conversion rate, and repeat purchase rate. What we found is that conversion rate did not decrease and the types of diners that were creating guest accounts historically began creating real accounts. What we also found is that our repeat purchase rate went back up. Something about selling the value of GrubHub as a service and not just a one time transaction made these people stick around. This was the first time we learned that something we did to make it harder to acquire a new customer could actually beneficial for our business over the long term.

We had a similar issue on the paid marketing side once at GrubHub. It might sound silly now, but group buying sites were all the rage as distribution channels a few years ago. The organization really wanted to try it as a way to boost demand in emerging markets for us. We had never really discounted our product before, so there were some serious concerns as to the long term effect of that, but we tried it anyway.

We gave $10 off $20 worth of food at any restaurant, and we got a ton of new customers. Those that used the coupons the first week also became great customers of GrubHub. Our one month repeat purchase rate for them was great. The company quickly wanted to scale the program to other cities. But I had recently instituted a new way to track any marketing initiative. The process was to track every initiative after one week, one month, and six months. Our one week and one month data was great, but when we did our six month look back on these sites, the data changed. What happened was that the people that used the coupon immediately were still good customers, but the many people who waited until the coupon was expiring to use it were not. They never returned. So, the overall program had a poor LTV that did not justify the acquisition costs.

I highlight these two examples to point out that it’s easy to sacrifice the long term growth of a startup with short term measurement and initiatives. Don’t be fooled. Measure everything you’re doing on short and long term effects, and think about the long term impact of something you might implement to shoot up short term numbers. Don’t let acquisition zeal ruin your retention. The one week, one month, six month rule is a great way to prevent short term growth decisions from taking you down a negative path. Find out what those cycles are for your business, and be diligent about measuring them for any acquisition channel you try.

Three Phases of Scaling Startup Marketing

Quite a few people have asked me recently how to scale their marketing efforts. The short answer is: as leanly as possible. I have realized that while many entrepreneurs intuitively understand that statement, they do not understand what that means in terms of what types of marketing you try and in what order. I have developed a framework to easily identify how to think about this process that scales across different types of startups that I will present below.

Pre-amble I: The Three Costs

The first thing that’s important to understand before delving into this framework is that lean = with as little cost as possible, and that costs come in three areas: marketing expenses, development expenses, and payroll expenses. Marketing expenses are pretty easy to explain. If you spend $5,000 on Google AdWords this month, and each ad promised $5 off, your marketing expenses are the $5,000 for AdWords plus $5 times the numbers of conversions that redeemed the $5 off promotion. Development expenses are a little more difficult. If you outsource development, it may be easy to think that the development expenses are how you paid your developers, but that would be incorrect. The real issue here is the opportunity cost. Whether you have your own developers or outsource, if your developers work on, say, SEO initiatives, that is time they are not spending on new features for your customers, infrastructure scalability, et al. The third cost is payroll expenses. This is the cost relating to paying the marketing people on your team. Early on in startups, payroll can be the largest expense, so adding people to your team, especially in marketing, needs to be carefully considered as payroll expenses are harder to adjust than marketing or development expenses. The only way to adjust them is to fire someone or reduce their salary, and both can have a negative effect on employee morale and company culture.

Pre-amble II: The Metrics

After you understand costs, you need to understand the metrics on how to evaluate marketing decisions. For startups where a customer pays you for a product or service, this is a little bit easier than if you have yet to determine a business model. A couple key metrics for me (forgive me if some of this is basic):

CPA (Cost per Acquisition): For this metric, you take the amount you spend in marketing expenses, and divide it by the number of new customers you acquired. So, in our previous AdWords example, let’s say I acquired 500 customers with that effort. That would make my CPA:
Cost: $5,000 + ($5 * 500) = $7,500
New Customers: 500
CPA: Costs/New Customers: $15

Revenue/User/Time frame: The CPA is not very meaningful until you know how it compares to the revenue those new customers brought you. The important thing here is not to just compare the revenue of the initial sales of that customer if they are likely to purchase again. If they are likely to purchase again, use cohort analysis to determine how much in revenue those new customers will make you over a certain time frame. The obvious question is how long a time frame should I use to compare against CPA. The short answer is as long you can reasonably predict. So, if you have enough data for a marketing channel to accurately predict how much a new customers from a new channel will make for your business over a two year time frame, you should feel comfortable comparing to a two year value. In reality, a startup almost never will have the data to accurately predict that far ahead. At the start, you may only have three months of reliable data. Reliable means both statistically significant i.e. not just 12 customers, but a reasonably sized population, and a situation where historical customers are representative of the newest customers. Once a startup reaches some scale, the latter requirement is the hardest. As a startup attracts more and more new customers, it typically has to start targeting customers that are less likely be early adopters and less likely to experience the pain your product solves as acutely as those who found it early on. Both of which will likely make new customers today less valuable than new customers from a year ago, keeping all other variables static. I almost always advise startups to keep their revenue metrics to a year or lower. After a year, you are waiting a very long time to prove your assumptions correct, and need to start discounting for the time value of money.

Volume: This basically asks is a marketing channel generating 12 customers or 1,200. This will determine how to prioritize resources among marketing channels.

Potential: This asks if a marketing channel has room to grow, and just needs more budget/people/development resources to achieve that potential, or is it already maxed out.

Marketing Profit: In this sense, profit equates to Revenue/User – CPA. Channels that have high Marketing Profit are going to be the areas you want to invest more time/money/people in, if the potential has not been reached and the volume is significant.

Note: Some marketers may want to add in the promo costs not in CPA, but subtract it from the revenue side. I dislike this approach, as it increases variability on both sides of the CPA vs. ARPU model, which makes it harder to compare the effectiveness of specific marketing channels just by looking at CPA.

Phase 1: Product Driven Growth
Scalable, Measurable, Engineering Opportunity Cost Driven

The leanest way to acquire customers is not to spend any money on them and not spend any money on marketing people. The chief ways to do that are to use the product you have already built to acquire more customers. There are four main ways:

Product Quality: This is the case where your product is so damn amazing that every person that uses it naturally tells everyone they know about it, and that’s what drives growth. In another, this is the build it and they will come strategy. Also known as a pipe dream. But improving the core product every day helps grow it, and people sometimes forget that.

Search Engine Optimization: This is the process of designing your site to appeal to search engines to rank for relevant queries to your business. Depending on the business, this may be a large opportunity or a very small one, and a very crowded space or a relatively sparse space. The main question to ask is: are people on search engines currently searching for keywords that closely match the product or service I provide? If so, can I reasonably expect that by designing by site using Google best practices with some well placed content that I could rank in the top five for any keywords that in aggregate would drive a meaningful amount of new customers? You may offer a product with a ton of keywords, but heavily competitive for SEO e.g. consumer real estate, or a product with no keyword volume, but also sparse for SEO e.g. a technical solution for resin casting.

Referrals/Viral Loops: This is the tactic of asking current customers to invite others to the service, perhaps offering a financial or psychological incentive. This is a fairly low development tactic with high upside and little downside, so it is very popular.

Conversion Rate Optimization: This is the process of making continual changes to a website/mobile app/landing page with the goal of increasing the amount of visitors who turn into conversions. The only costs here are development costs, but the effects are very measurable. This may be difficult to do until you have enough traffic on your site accurately measure lifts in conversion rate.

Main constraint: Development resources
What should happen here: You start here, iterate as much as you can until your development pipeline gets too clogged. Then, as you wait for development to catch up, you move onto Phase 2.

Phase 2: Performance Marketing
Measurable, Scalable, Marketing Budget Driven

The next leanest way to acquire customers is to develop marketing systems that, once created, can scale from driving dozens of new customers to thousands with the only additional input being more marketing expenses. Early marketers in a startup can focus on marketing efforts that scale without having to hire more marketers and have vast potential. If you don’t have a business model yet, most of these tactics (besides email or push notifications) will be off limits, as you will have no Revenue/User/Time frame to compare CPA’s against.

Search Engine Marketing: This is targeted specific keywords on Google and Bing and bidding to show an ad for your product or service to potential new customers at the top of the page. Like SEO, you need to determine if the search engines have enough search volume to make this an effective channel. You also need to determine how expensive it is for you to bid on keywords and convert them. If there is a lot of search volume and not a lot of competition, this can be a very effective way to drive customers, and is very trackable to CPA goals down to the ad and keyword level.

Email Marketing: This is sending either mass or, preferably, targeted and personalized emails to your existing user base to either convert them into a paying customer or entice repeat purchases. This is probably the most cost-effective and under-utilized tactic for most startups.

Online/Mobile Display Advertising: This is showing banner ads on websites and mobile apps. Most startups use retargeting to reach people that have already been to their site but not converted. More sophisticated startups are experimenting with real time bidding to find ad impressions that are likely to reach their target market. The challenge here is determining effectiveness of spend as few people click ads, and correlating views to purchases is a dicey proposition. There is near limitless inventory to spend on if you can determine effectiveness.

Main constraint: Money and diminishing returns
What should happen here: You experiment with a few paid, scalable channels, find the ones that work, and scale them until you see diminishing returns. If your team still has bandwidth, then you have them contribute with Phase 3 tactics. If your team doesn’t have bandwidth to scale these techniques, but they work, you should hire a dedicated resource for them.

Phase 3: Brand Marketing
Non-Measurable, Non-Scalable, People Driven

These are techniques that have one-time value, and/or require not just increased investment, but also increased resources (read: people or development) to scale. They are also frequently going to only work in one market or for one type of customer.

Content marketing: Content marketing has many names, but is the creation of blog posts, articles, videos, of infographics that are of interest to your target customer. They can be great content to help rank for SEO, especially if your business does not have more direct keywords to target. It is even more impactful as content for social media or distribution to media outlets. It is non-scalable because if you want to do more of it, you have to produce more of it, and it’s time consuming to do it well unless it’s user-generated.

Out of home: This is the process of buying billboards, bus shelters et al. out in the real world and placing ads for your business in them. This can be very expensive, but also very valuable if there’s a way to use them to target a very valuable audience e.g. hungry urban professionals right before dinner in GrubHub’s case. It’s non-scalabale because only certain geographies will have good opportunities, and what will work in one geography may not even be an option in another.

Community management: This is either using a section of your site/app or more likely social media channels such as Twitter and Facebook to communicate with your audience in conversations they are interested in having with you. This is a great way to provide quality customer service and create evangelists. It is non-scalable as it requires a dedicated resource to monitor these channels, and as you scale, to keep up quality, you need to add more dedicated people for it.

Public relations: This is the process of getting news outlets to showcase you. It is non-scalable because it provides a bump, not an engine, requiring constant efforts to make it a consistent source of traffic.

Marketing promotions: This is the process of a creating a compelling campaign that attracts people to your business, whether it’s a contest, event, etc.

Main constraint: People
What should happen here: Use your existing team to determine which of these is important for your company, and then staff accordingly.

Running All Three Phases

These phases do not imply that you focus all of your efforts on phase I, max out, and then move onto the next phase. The reality is that the constraints of the various phases set in quickly. So, it could that in close to no time at all, you are executing programs in all phases. This blog post just provides the framework for prioritization and best practices for maximizing how you grow your marketing programs.