Over the years of working with different Amazon Seller clients as an Agency, we came across the budget limited request on a few occasions. Here is how to do Amazon PPC management when there is a limited budget spend.
Not just with starting sellers
The request is not just the starting Sellers (who still have their Amazon sales cashflow to be straightened out) who ask for this. A number of hitched and mono-product Sellers can fall back to this method of budgeting, die to their quantitative cash flow structure.
Amazon PPC Management with Limited Budget Request Must Use Greater PPC Skills
Either way, this kind of restriction of Amazon PPC calls for certain changes to be made to the classic PPC Campaign Structure. After all, you will be deliberately limiting the number of impressions your Ads will be receiving.
It’s best for business if you make all of these impressions count.
Step 1: Three Keystones of Good Amazon PPC Ads
⚠️ Spoiler alert — budget is one of them.
To build a strong foundation for a truly effective Amazon PPC Campaign, there are 3 important keystones. Only when worked on together, will they allow a Seller to make the most out of a limited budget.
Amazon Product Detail Page: Quality and Theme
Amazon.com is about letting buyers meet their perfect product. In fact, we’ve got a whole separate article like this on Amazon SEO. But to keep things simple, Amazon’s algorithm will be more likely to regard your ASIN as a good product if you help it out:
- The algorithm scans text on the Product Detail Page to index the ASIN against these keywords. Specifically, it draws on:
- At least 256 characters from product Title
- First 100 characters of every Bullet Point
- 250 characters from Backend Keywords
If you want to use specific keywords in your limited budget ads — make sure they are indexed. This helps to boost keyword ranking a lot from the get-go.
Product Picture 1. A good picture (that clearly shows your product will catch more eyes out of all customers who were searching for something like it. Thus — better Click Through Rate (CTR). And the algorithm takes note of good CTRs.
⚠️ Remember: a picture is considered “good” if the product is visible in any adverse conditions. Like on a small smartphone screen, outside, and on a summer sunny day.
Amazon Product Reviews. Other things being equal, be considered trustworthy, an ASIN should have at least 1/10th of the average number of reviews that top 5 competitors boast. If it’s less at the moment — the product will likely be losing some conversions from the more risk-avert customers. Even if the product itself is good.
The Amazon PPC Budget
Since this article is all about running Amazon PPC Ads with a limited budget, a Seller will have to work around some limitations manually. For example — she’ll have to run a larger/ a few shorter keyword research sessions at the beginning of the PPC Campaign.
Since the limited budget would not give her the luxury of running broad match or Auto campaigns and then study the Search Term Report to get those new targeting keywords and negative keywords out of it.
Amazon Relevant Keyword Research: Targeting Keywords and ASINs
Collecting a pool of relevant target keywords/ASINs is fundamental for the success of a limited budget for the Amazon PPC Campaign.
The more (out of hypothetical 100%) relevant keywords/ASINs you find during the research phase — the more you will have to choose from. In fact, this is our Step 2 in preparing to run a tight budget Amazon PPC Campaign.
Step 2: Amazon Keyword Research
Think of it this way: people are already using some words to describe your product. Some search terms used by the average person are pinpoint accurate. Other people use vague ideas and terms when they search. The individual can mean several things at once — but these search terms are used by real people who DO want to find a product like yours nonetheless.
Amazon.com is not easy on giving out those search terms everyone is using. It will only let you know what search terms people used to find your paid Sponsored Ads (via Search Term Report).
Naturally, if you are making your first Sponsored Product campaign — you just don’t have that yet. With a limited budget — you aren’t likely to get a too rich one in the future either.
Profound keyword research is the major step in preparing for a successful limited budget PPC campaign, as is knowing your negative keywords.
We assume that tight Ads funding indicates that you will also be limited in finances for the starting research. But just in case you are not, remember: there are a few perfectly viable keyword research tools that can help you cut some corners. For a price. “Merchant Words,” “Helium 10,” to name a few. Try them.
Whichever way you have chosen to get a hold of a pool of relevant potential keywords, they need to be tried out and tested in combo with your specific product before you are confident that you ARE leaving only the truly best-performing ones.
Unfortunately, some budget WILL have to be spent on this trial-and-error — there is no way around this.
But there is a way to make it worthwhile.
Step 3: The Magic of Amazon PPC Optimization
What is a good targeting keyword/ASIN?
ASIN is the one that generates sales at an acceptable cost. As simple as this.
Once you’ve completed step 2 — you should prepare for testing your discoveries. We are subject to make a clear and Strategy-oriented Amazon PPC Campaign Structure (see: profitwhales dot com, amazon campaign structure) from the get-go.
You may opt to disable the campaigns and ad groups that were built around keywords with worse than expected performance (leave them for later tests), but at least you’ll keep them in order.
As for the more aspiring keywords — they must be tried and tested.
We suggest running ads for at least a 7-day period before diving into the results to analyze.
Suppose the target keyword/ASIN in question is a good candidate for full-time Limited Ad Budget allocation. Here’s how we do it:
Look at the Impressions.
Impressions are governed by how many people have entered a search term that has triggered your keyword over a given period.
The other factor is the bid: set it too low, and many customers who made it to the Search Results page — may end up being abysmal.
But if you see some good figures (e.g., a hundred impressions a week) — then that keyword is valid. The only question now is…
What’s the CTR?
CTR of a keyword indicates how relevant your product title and your Pic 1 is to the text a customer has just typed into her Amazon search console.
Across many incidences, we’ve come to see that:
- CTR below 0.25% is indicative of an irrelevant keyword (or a VERY strong competition present);
- CTR between 0,25% and 1% means a keyword is reasonable;
- CTR above 1% means that the keyword is highly relevant.
Remember that showing you Ads on Top of Search placement changes all the CTR values dramatically. If that is where your product Ads end up — multiply the above approximate CTR thresholds by 3 to judge the quality of the keyword.
Now that we’ve picked the more product-relevant keywords, we need to see…
How well CTR converts (at what ACoS).
This is where we start separating the weeds from the grain.
Here’s how to make the keyword judgment call:
- If a keyword had 10-15 clocks and no sales — discard (pause) it;
- If there is a sale, you will need to wait for at least 3 (better 5) sales to make a further judgment, so be patient. Some long-tail, extremely relevant keywords could become a great and cheap sales-generating asset for your limited budget Amazon PPC Campaign. Even if they do generate 5 sales a month — it’s worth waiting to find out for certain;
- Any keywords that do generate sales but at ACoS 80%+ should be discarded (paused);
- For any keywords with ACoS between 80% and your break-even ACoS — it may be worthwhile to lower the bid and have them slow-burning. They are likely to generate you sales in the future at a reasonable ACoS each;
- For any keywords that perform at ACoS 5% or less than your break-even level… congrats. These are the candidates to go into your strict budget top-performing exact match type keyword campaigns. They will be the ones that will generate the bulk of your PPC sales.
Panning for Amazon Gold
You can make money on Amazon even with a limited budget, and that is your version of panning for the gold.
The above keyword testing is the only real way to confirm that every keyword’s worth it for your product and market circumstances.
Ensure you are giving at least 20% of that limited budget to running more keywords through the test. And direct the other 80% to feed the campaigns that contain already-verified ones that will generate you sales.
You can meet your business goals even within a budget.
Using careful product testing, you will always be on your business goals. This will allow you to hang on and not let go of a chance to eventually discover an even better targeting option that will be perfect for your ASIN.
Image Credit: karolina grabowska; pexels; thank you!
How Preql is Transforming Data Transformation
More than one million small businesses use ecommerce platform Shopify to reach a global audience of consumers. That includes direct-to-consumer (DTC) all-stars like Allbirds, Rothy’s and Beefcake Swimwear.
But online sellers like these are also ingesting data from platforms like Google Analytics, Klaviyo, Attentive and Facebook Ads, which quickly complicates weekly reporting.
That’s where data transformation comes in.
dbt and Preql
As the name implies, data transformation tools help convert data from its raw format to clean, usable data that enables analytics and reporting. Centralizing and storing data is easier than it’s ever been, but creating reporting-ready datasets requires aligning on business definitions, designing output tables, and encoding logic into a series of interdependent SQL scripts, or “transformations.” Businesses are making significant investments in data infrastructure tooling, such as ingestion tools, data storage, and visualization/BI without having the internal expertise to transform their data effectively. But they quickly learn if you can’t effectively structure your data for reporting, they won’t get value from the data they’re storing—or the investment they’ve made.
The space includes two major players: dbt and startups.
Founded in 2016, dbt “built the primary tool in the analytics engineering toolbox,” as the company says, and it is now used by more than 9,000 companies—and it is backed by more than $414 million.
But dbt is a tool for developers at companies with established analytics engineering teams.
Preql, on the other hand, is a startup building no-code data transformation tool that targets business users who might not have expertise in programming languages but who nevertheless need trusted, accessible data.
Preql’s goal is to automate the hardest, most time-intensive steps in the data transformation process so businesses can be up and running within days as opposed to the six- to 12-month window for other tools.
“We built Preql because the transformation layer is the most critical part of the data stack, but the resources and talent required to manage it make reliable reporting and analytics inaccessible for companies without large data functions,” said Gabi Steele, co-founder and co-CEO of Preql.
The startup is therefore positioning itself as an alternative to hiring full analytics engineering teams solely to model and manage business definitions—especially among early-stage companies that are first building out their data capabilities.
In other words, Preql is the buffer between the engineering team and the people who actually need to use the data.
“Data teams tend to be highly reactive. The business is constantly asking for data to guide decision making, but in the current transformation ecosystem, even small changes to data models require time and expertise. If business users can truly manage their own metrics, data talent will be able to step out of the constant back and forth of fulfilling reporting requests and focus on more sophisticated analyses,” said Leah Weiss, co-founder and co-CEO of Preql.
But that’s not to say dbt and Preql are bitter rivals. In fact, they are part of the same data transformation community—and there’s a forthcoming integration.
“One way to think about it is we want to help the organizations get up and running really quickly and get the time to value from the data they’re already collecting and storing without having to have the specialized talent that’s really well versed in dbt,” Steele added. “But as these companies become more sophisticated, we will be outputting dbt, so they can leverage it if that’s the tool that they’re most comfortable with.”
A Closer Look at Preql
The startup raised a $7 million seed round in May, led by Bessemer Venture Partners, with participation from Felicis.
Preql collects business context and metric definitions and then abstracts away the data transformation process. It helps organizations get up and running with a central source of truth for reporting without having a data team or writing SQL.
Preql reads in data from the warehouse and writes back clean, reporting-ready schemas. It partners with data ingestion tools that move data from source applications into the warehouse such as Airbyte and Fivetran and cloud data warehouses like Snowflake, Redshift and BigQuery. For businesses who consume data in BI tools, it also partners with Looker, Tableau and Sigma Computing.
Preql is initially focused on the DTC market in part because the metrics, such as cost of customer acquisition (CAC), conversion rate and life-time value (LTV), are standardized. They also tend to have lean operations.
“We’ve found that these companies are working really hard to download data from disparate sources—third-party platforms that they use, Shopify, their paid marketing platforms—in order to get a sense of even basic business health and performance,” Weiss said.
They also tend to use manual reporting processes, which means “it’s often an operations person who’s downloading data from a bunch of sources, consolidating that in spreadsheets, making a bunch of manual interventions and then outputting weekly reporting or quarterly reporting,” she added.
But much of what these companies want to measure about performance is consistent and a lot of the data sources are structured the same way.
“With Preql, we were able to make some assumptions about what we wanted to measure with the flexibility to customize a few of those definitions that are specific to our business,” added Cynthia Plotch, co-founder at Stix, a women’s health essentials ecommerce site. “Preql gave us clean, usable data for reporting. We were up and running with weekly reporting within days, saving us months of effort if we had to invest in data engineering teams.”
Data Transformation in 2027
Steele and Weiss believe the next five years will be about “delivering on the promise of the modern data stack.”
In other words, answering questions like: Now that we have scalable storage and ingestion, how can we make sure we can actually leverage data for decision making? And how can we build trust in reporting so we can build workflows around it and act on it?
This is because a lot of companies struggle to move on to predictive analytics and machine learning because they never solved the fundamental issue of creating trusted, accessible data.
What’s more, Preql believes the next phase of tools will go beyond building infrastructure to deliver more value as data talent sits closer and closer to the business.
“Data analytics will only get more complicated because the number of data sources is growing, along with their complexity, and the need is becoming more acute for real time results. And the more data you have, the more granular the questions become and even more is expected of it,” Amit Karp, partner at Bessemer Venture Partners added. “I think we’re in the very early innings of what’s going to be a very long wave—five, ten or even 20 years down the road. It’s a giant market.”
Can Traditional Companies Act Like Start-Ups?
Much has been made about the culture clash between older, slower, more traditional companies and younger, more dynamic, faster-moving tech start-ups. Each has advantages and disadvantages, but, generally speaking, it is very hard to reconcile the two approaches, as they are naturally in opposition to each other.
The general motto among start-ups of “move fast and break things” has led to very quick yet massive successes, with some companies, Google and Amazon being the most obvious examples, growing larger than traditional competitors who have been around for decades and decades. But it has also led to a lot of unconsidered damage to traditional industries like transportation and publishing, their ‘disruption’ doing as much harm as good. And, more often than not, start-ups can see millions or even billions in investment being wasted on bad ideas and unproven tech (Theranos, anyone?). “Fake it till you make it” means that, eventually, you actually do need to make it.
Meanwhile, traditional companies, while providing more useful and regular forms of employment, great institutional knowledge, and decades of business experience, have their own problems. Because they often resemble large, inefficient bureaucracies, they are slow to move and respond to change. Old companies can be blind to, and even fearful of, innovation and new technology. This can leave them dead in the water when the future finally arrives. Kodak, for example, went from venerated, dominant business to almost nothing in just a few years because it refused to accept the revolution of digital photography.
But is there a way to integrate the two approaches? To take the best from both cultures and business plans and use those aspects to move into the future? To get big, old businesses to work, at least in some ways, like small, agile, young start-ups? Yes, but it isn’t easy.
Innovation Without Disruption
As stated, one of the greatest fears of traditional companies is having their business, or their entire sector, undercut by a growing start-up. While independent start-ups are expected to disrupt, be change agents, or however you want to put it, more traditional companies are prone to be much more risk averse. Naturally, one of the smartest things that an old company can do to avoid being left behind is to lead the disruption themselves.
Many traditional businesses are currently investing in, and should continue to invest in, the digital transformation of their business model, from top to bottom. This, however, is a slow process, especially in sizable companies. The use of machine learning, predictive analysis, AI, and other cutting edge digital tools allows old business models to become more efficient, and respond to changes in supply and demand, and market tumult, in better and smarter ways. But it isn’t as easy as flipping a switch.
A New Business to Try New Things
Quite a few traditional businesses are spinning out new sectors, tech labs, and other separate silos to do the work of digital innovation for them. This isn’t uncommon. Businesses have, basically forever, had subsidiaries. The problem is that old businesses have trouble actually committing to the idea.
Often, the business that is spun-out is, essentially, a temporary one. The leaders of the core business get cold feet, limit the new project’s mandate, and pull it back in as soon as possible. Such hesitance is limiting in today’s digital world, where the next revolutionary innovation is always just around the corner.
Furthermore, spin-outs with good ideas and potential for growth are frequently allowed to die on the vine, just as often they go to seed. Or, to make things clearer, the core business doesn’t invest in the digital spin-out’s success. The great advance of digital companies is their ability to scale with almost lightning speed. But core business have to be ready with resources and support for the scale-up to even happen, let alone work. Otherwise, a grand opportunity will go to waste.
If a business spin-out does well enough, it should be allowed to grow and change as it needs to, provided that it remains successful and worthwhile. Whether the goal is for the new business to simply make money in an area the core business isn’t directly addressing, or developing digital innovations for the core business to take up, if it works it works. Don’t get in the way of success just because it is new, or comes in an unfamiliar form. At the same time, core businesses must be careful of how they measure success for these new experiments. Measuring the new company or spin-out with the same metrics as the core business can sometimes choke the momentum and not give an accurate picture. Afterall, newer, smaller businesses, or initiatives shouldn’t be expected to be profitable immediately.
Cultural Change, From the Executive Level On Down
All the innovation in the world won’t mean anything if the people running the business itself refuse to change. Older companies, and older executives, can become set in their ways, dismissive of new technologies and ways of doing business, and ignore the automation and efficiencies of advanced digital tools. We saw this at the beginning of the widespread use of the internet twenty years ago, and we’re seeing it now.
More important than this, is the need for people in positions of real power in companies to implement the changes needed for innovation and advancement, and do so thoroughly and effectively. There must be a willingness to let the start-up culture infiltrate and influence the way business is done at every level, or it won’t be effective enough to help.
It is painfully common for large, traditional companies to put money into research and development of new ideas and new technologies, only for executives and other decision makers to ignore what’s in front of them, either because of cost, or risk, or something as simple as a fear of the future.
But the future of business is changing in a digital world. Things move and change with an almost frightening speed. The Covid-19 pandemic is absolute proof of that; it wasn’t just companies with digital tools at the ready that were able to survive. While they had an advantage, it was the companies that were able to acknowledge the rapidly changing situation, and react to it quickly and efficiently, that kept things going and in some cases, even improved their bottom lines.
But It’s More Than Just a Cultural Change
One of the biggest advantages of tech start up culture is that it is forward-facing. It is an attitude towards business and technology that is not just looking towards the future (every business does that), but is actively trying to grapple with it, and even to shape it, if possible. Traditional, legacy businesses need to admit that the world is not static, and they have a responsibility in influencing how their industry develops.
Part of that responsibility is letting innovators be innovators. If a large company spins out a business unit to study and improve its digital technology, that company can’t then balk when those innovators recommend widespread change, or create a new idea that could shake the company, or its whole industry, to its core.
To put it as simply as possible, for an older, more traditional company to reap the benefits of adopting a start-up model, it has to actually adopt it. It can’t just make superficial changes, it needs to truly invest. But that kind of investment carries risk, which can make more traditional companies nervous. The work of transformation must actually be done.
That means supporting digital innovations and changes when they make things more efficient. It means letting spin-out businesses actually try new things, and grow to scale when they hit upon something new and successful. It means executives getting out of the way so the forces of change can actually, you know, change things. Otherwise, the ‘traditional’ company will just be the ‘old’ company, sitting around waiting for some new tech upstart to disrupt it into obsolescence.
Understanding Edge Computing and Why it Matters to Businesses Today
The edge computing market is expected to reach $274 billion by 2025, focusing on segments like the internet of things, public cloud services, and patents and standards.
Most of this contribution is backed by enterprises shifting their data centers to the cloud. This has enabled enterprises to move beyond cloud systems to edge computing systems and extract the maximum potential from their computing resources.
This blog will provide a closer understanding of edge computing and how it helps businesses in the technology sector.
Understanding edge computing
From a technical standpoint, edge computing is a distributed computing framework that bridges the gap between enterprise applications and data sources, including IoT devices or local edge servers.
For an easier understanding, edge computing helps businesses recreate experiences for people and profitability through improved response time and bandwidth availability.
Why does edge computing matter for businesses?
When we talk about the most significant industry zones worldwide, for instance, the GCC region, which is heavily focused on the focus areas like cloud services, the transition from cloud technology to edge computing is now more prominent than ever for enterprises to leverage the potential of the technology.
And with only 3% of businesses at an advanced stage in digital transformation initiatives, the potential of edge computing is up for grabs.
It doesn’t matter if you’re running a mobile app development company, a grocery store next door, or a next-gen enterprise. You need to understand how cloud edge helps businesses and invest in this open-source technology.
Edge computing is primarily sought in industries where value-added assets have a massive impact on the business in case of losses.
The technology has enabled reports delivery systems to send and receive documentation in seconds, usually taking days to weeks.
Consider the example of the oil and gas industry, where some enterprises utilize edge computing. The predictive maintenance allowed them to proactively manage their pipeline and locate the underlying issues to prevent any accumulated problems.
Support for remote operations
The pandemic has forced businesses to opt for remote operations, or a hybrid work model at the least, with the workforce, spread across different geographical boundaries.
This drastic shift has brought in the use of edge apps that would permit employees to secure access to their organization’s official servers and systems.
Edge computing helps remote operations and hybrid teams by reducing the amount of data volume commuting via networks, providing computing density and adaptability, limiting data redundancy, and helping users comply with compliance and regulatory guidelines.
Faster response time
Businesses can enjoy lower latency by deploying computational processes near edge devices. For instance, employees typically experience delays when corresponding with their colleagues on another floor due to a server connected in any part of the world.
While an edge computing application would route data transfer across the office premises, lower the delays, and considerably save bandwidth at the same time.
You can quickly scale this example of in-office communication to the fact that around 50% of data created by businesses worldwide gets created outside the cloud. Putting it simply, edge computing allows instant transmission of data.
Robust data security
According to Statista, by 2025, global data production is expected to exceed 180 zettabytes. However, the data security concerns will equally increase proportionately.
And with businesses producing and relying on data more than ever, edge computing is a solid prospect to process large amounts of data sets more efficiently and securely when done near the data source.
When businesses take the cloud as their sole savior for data storage in a single centralized location, it opens up risks for hacking and phishing activities.
On the other hand, an edge-computing architecture puts an extra layer of security as it doesn’t depend on a single point of storage or application. In fact, it is distributed to different devices.
In case of a hack or phishing attempt, a single compromised component of the network can be disconnected from the rest of the network, preventing a complete shutdown.
Convenient IoT adoption
Global IoT spending is expected to surpass $410 billion by 2025. For businesses, especially in the manufacturing sector, who rely on connected technology, the internet of things is at the thickest of things in the global industry today.
Such organizations are on the constant hunt to up their computational potential and probe into IoT through a more dedicated data center.
The adoption of edge computing makes the subsequent adoption of enterprise IoT quite cheap and puts little stress on the network’s bandwidth.
Businesses with computational prowess can leverage the IoT market without adding any major infrastructure expenses.
Lower IT costs
The global IT spending on devices, enterprise software, and communication services rose from $4.21 trillion to $4.43 trillion in 2022. While a considerable share of the global spending accounts for cloud solutions, obviously as the pandemic has only pushed the remote operations and hybrid working model further up.
When users keep the data physically closer to the network’s edge, the cost of sending the data to the cloud reduces. Consequently, it encourages businesses to save on IT expenses.
Besides cutting costs, edge computing also contributes to helping businesses increase their ROI through enhanced data transmission speed and improved networks needed to experiment with new models.
How is edge computing different from cloud computing?
Although edge computing and cloud computing are each other’s counterparts for data storage and distribution, there are some key differences regarding the user’s context.
Edge computing deploys resources at the point where data generates. In contrast, cloud computing deploys resources at global locations.
Edge computing operates in a decentralized fashion, while cloud computing is centralized.
Edge is made on a stable architecture, and cloud resources are made on loose-coupled components.
Edge-based resources respond instantaneously, and cloud resources have a higher response time.
Edge computing requires lower bandwidth, while the cloud counterpart consumes a higher bandwidth.
Although, the above difference makes edge computing a clear winner in all aspects for any business. But there’s a catch!
Suppose your business resides at multiple physical locations, and you need a lower latency network to promptly cater to your customers who are away from your on-prem location. In that case, edge computing is the right choice for you.
Top edge computing use cases
Although there are numerous examples of edge computing use cases, I’ll talk about a few that I find the most interesting.
Autonomous flocking of truck convoys is the easiest example we can come for autonomous vehicles. With the entire fleet traveling close while saving fuel expenses and limiting congestion, edge computing has the power to eliminate the needs of all the drivers except the one in the front vehicle.
The idea being the trucks will be able to communicate with the others via low latency.
Remote monitoring of oil and gas industry assets
Oil and gas accidents have proved catastrophic throughout the industry’s history. This requires extreme vigilance when monitoring the assets.
Although oil and gas assets are placed at remote locations, the edge computing technology facilitates real-time analytics with processing closer to the asset, indicating less dependency on high-quality connectivity to a centralized cloud.
Edge computing is on course to elevate the adoption of smart grids, enabling enterprises to handle their energy consumption better.
Modern factories, plants, and office buildings use edge platform-connected sensors and IoT devices to observe energy usage and examine their consumption in real-time.
The data from real-time analytics will aid energy management companies in creating suitable, efficient workarounds. For example, watching where high energy consumption machinery runs during off-peak hours for electricity demand.
Cloud gaming, seemingly the next-big-thing in the gaming business like Google Stadia, PlayStation Now, etc., dramatically leans on latency.
Moreover, cloud gaming companies are on the quest to build edge servers as close to gamers as possible to reduce latency and provide a fully immersive, glitch-less experience.
This concludes our discussion on understanding edge computing and how it matters for enterprises worldwide.
Now that you understand the benefits of edge computing and its applications in different industries and use cases, it is evident that it’s a great value proposition for businesses that want to acquire competitive advantages and lead their spaces from the front line.
Featured Image Credit: Provided by the Author; Thank you!