Frequency benchmarks can also vary by channel. Below we took the frequency cliff of several e-commerce brands to set benchmarks by channel:
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Next we're going to run through how you can fine-tune your frequency preferences step by step, with the ultimate goal of maximizing ROI. We want to emphasize that this is an iterative process ' you should review these preferences regularly to improve your results and drive more positive customer experiences. You can (and should) also augment this process by letting machine learning models crunch all your data, freeing you up to think contextually and intuitively.
Whenever possible, we recommend optimizing toward measurable business outcomes like ROI or ROAS ' that's where this strategy works best. At the very least, you should aim to calibrate frequency based on CPA goals, which reflect a major milestone in the customer experience (e.g., building a car, submitting a quote, or downloading a white paper). Marketers who have no goal, or simple reach goals, tend to focus on effective CPM (eCPM), which emphasizes buying the cheapest inventory. This has consequences for brand performance though, because cheap inventory often fails to capture the right consumers' attention.
By moving away from cost and focusing instead on the value of media, you can foster better consumer experiences. If your brand is already focused on business results, then it's a perfect time to reflect on what 'good' performance looks like and the price tolerance you should expect. While we won't get into how to set KPI and eCPM thresholds, we do want to underscore that it's best practice to analyze the relationship between price and performance. Plotting out performance (e.g., CPA) relative to cost (eCPM) can uncover pockets of inventory that are not efficiently delivering outcomes. Conversely, inventory with strong performance could benefit from more volume and higher CPMs to drive incremental performance.
Generally speaking, machine learning algorithms (like Koa', The Trade Desk's artificial intelligence) do a fantastic job of analyzing and optimizing the relationship between predicted performance and the cost of individual impressions. These algorithms are purpose-built to determine the best bidding and CPM strategies to maximize your performance. So you can analyze where the algorithms are finding value and use those insights to unlock further opportunity.
For two examples, let's look at Figure 3 above. The plot points within circle A represent high-performing impressions. For this type of low-cost inventory, you may want to focus on maximizing scale and potentially increasing bids to help uncover pockets of inventory that might not have been considered previously. By contrast, the plot points within circle B show us low-performance, high-cost inventory. For this you can reach into your supply path optimization toolbox and try to shape your buying strategies away from those pockets of impressions.
For inventory that falls outside of circles A and B ' well-performing supply directly in line with your performance goals ' you can employ frequency-calibration practices, which we outline below.
As we noted, frequency settings are specialized ' each brand, product, and campaign launch will have its own optimal frequency. If you have the luxury of analyzing previous campaign data, we recommend you use those insights to inform a new campaign and run a frequency analysis with recent data (e.g., within the last 14 days). For advertisers with limited campaign history, we suggest setting a reasonable frequency cap (using informed benchmarks) at the onset of a campaign, letting the campaign run, and analyzing results once there's enough time and data to review (typically after one to two weeks).
This next step is the focal point of the exercise: finding where the rate of advertising spend exceeds the growth rate of cumulative conversions. In other words, you need to figure out when focusing on frequency (hitting the same audiences again and again) might be less efficient than focusing on reaching net-new audiences (unique reach). This inflection point indicates the moment when increasing frequency may no longer produce incremental results. You can find this point by filtering performance into frequency buckets and identifying the relationship between advertising spend and conversion volume. When mapped out graphically, it can often look like a bell curve (pictured in Figure 2 above) or a more progressive slope with sharp outliers at higher frequency buckets. In either case, this exercise will help you uncover the variables that impact optimal frequency.
Next, analyze where minimum exposures are being made. (This typically happens in inventory-scarce environments.) You can use this as an opportunity to assess when a specific frequency correlates with higher performance. In this phase, CPMs should also be evaluated to ensure you're being competitive. The end goal of this phase is to determine a target minimum frequency and make sure the campaign can deliver close to that minimum across all supply. For most campaigns, frequency minimums can be set to 'lifetime' timelines.
By studying the time between impressions served, you can identify how close impression exposures need to be to drive maximum engagement, ad recall, and bottom-line performance. Delivering impressions too close together or too far apart can have an adverse impact on performance and customer experience. In this advanced step, savvy programmatic practitioners can pull in impression time-stamp data, cross-referencing the frequency buckets shown in Figure 2. This way, you can work toward finding the ideal distribution of impressions within frequency settings to bolster performance. While we won't dive into details on how to determine optimal impression distance, we have provided an illustrative analysis that can be replicated.
When purchasing a spectrum analyzer, choosing the right model for the correct application can be challenging. Not all spectrum analyzers are the same. The frequency range, level of accuracy, and measurement types can vary significantly from one model to the next.
Understanding the different spectrum analyzers available and their functions is essential to ensure you purchase the right one for your needs. We know you don't want to waste your time and money, so we created this guide to walk you through the basics of spectrum analyzers. Knowledge equals power, the power to make an informed purchase!
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A spectrum analyzer is a tool used to visualize and analyze the spectral content of signals. It accomplishes this by measuring the magnitude of the input signal versus its frequency over the entire frequency range.
Think of this as a means to see a signal's "frequency fingerprint." This fingerprint will help you determine the signal power of an instrument and how that signal makes up different frequency components.
Spectrum analyzers usually consist of a display, a sweep generator, and a filter.
Thanks to technological advances, modern spectrum analyzers are smaller and more affordable than ever. They are also more accurate and have more features than their predecessors.
Whether you are an engineer or a hobbyist experimenting with electronics, a spectrum analyzer is a valuable tool to have.
An engineer would use a spectrum analyzer to run tests on electronic equipment like cell phones, radios, and TV towers to ensure they function as intended with no interference from other devices. A hobbyist might use a spectrum analyzer to check the quality of an audio signal or track down interference sources.
While spectrum analyzers and oscilloscopes are both essential for understanding the behavior of electronic signals, they work in different ways.
Combining the two devices gives a more accurate picture of what is happening with a signal. So, in many cases, a spectrum analyzer and oscilloscope are necessary to fully understand a signal's behavior.
A spectrum analyzer has many applications. You can use a spectrum analyzer for radio, wireless communication, and audio engineering. They will also help troubleshoot electronic equipment andoptimize wireless systems.
Standard signal analysis applications include
Each type of spectrum analyzer has unique features. For example, some are better suited for analyzing radio signals, while others are more adept at measuring audio frequencies. The desired application will determine suitability.
Now that we have identified some key fundamentals of spectrum analyzers, let's take a look at some of the different types on the market. We will consider vector signal analyzers, swept spectrum analyzers, and real-time spectrum analyzers.
Vector Signal Analyzers (VSAs) allow you to design, validate, and debug communication systems. They provide insight into the behavior of these systems by providing measurements of the signal's amplitude, phase, and frequency.
VSAs also offer advanced analysis capabilities, such as
These capabilities make VSAs invaluable for ensuring communication systems meet their performance requirements. VSAs are available in various forms, including benchtop units, modular units, and hand-held units.
Vector Signal Analyzer Pros Vector Signal Analyzer Cons Measure the magnitude and phase of a signal. This is important because many real-world signals are not sinusoidal and have different amplitudes at varying frequencies. Limited frequency range. They can only measure signals at a specific frequency. Characterize complex signals. A complex signal can be a combination of multiple sinusoidal signals with different amplitudes, phases, and frequencies. Limited dynamic range. They can only accurately measure signals within a certain range of amplitudes. Combined with their restricted frequency range, this may be limiting for some applications. The ability to generate eye diagrams. These are graphical representations of how well an electrical signal conforms to certain standards. Eye diagrams are commonly used in digital communications systems to assess signal quality. Typically have a much higher learning curve than their traditional counterparts. New users may find it challenging to become proficient in their use.Swept spectrum analyzers sweep a sinusoidal signal across a frequency range RF (radio frequency) spectrum and measure the system's response under test at each frequency. A graph typically displays the results, with amplitude (y-axis) versus frequency (x-axis).
By observing the shape of the response curve, you can determine the system's center frequency, bandwidth, gain, and noise floor.
Swept spectrum analyzers troubleshoot and monitor RF systems, such as cellular communications, WiFi networks, satellite systems, and radar systems. They also measure the performance of RF components, such as filters, amplifiers, and antennas.
Swept Spectrum Analyzer Pros Swept Spectrum Analyzer Cons Faster and easier to use. They take measurements in just a few seconds, while traditional spectrum analyzers can take several minutes. Generally less sensitive than Fourier transform-based analyzers, making them less suitable for low-noise applications. More accurate. Swept spectrum analyzers combine multiple measurements taken at different frequencies to create a more precise picture of the signal's power. Traditional spectrum analyzers measure the power of a signal at each frequency within its bandwidth. The strength of a signal can vary significantly from one frequency to another, resulting in an inaccurate measurement. Subject to distortion due to how the signal spreads out over the frequency spectrum. Less expensive. As they use fewer components than a traditional analyzer, a swept spectrum analyzer is an excellent choice if you need an affordable analyzer that is accurate and easy to use. Requires a stable local oscillator. This can be challenging to generate for some frequencies.Real-time Spectrum Analyzers (RSA) are electronic devices that measure a signal's strength and frequency domain. They use a process called Fast Fourier Transform (FFT), which converts the signal into a series of numbers representing the amplitude of each frequency component.
As its name suggests, time domain signals depend on time, whereas frequency domain signals rely on frequency. The Fourier transform enables signal transformation from one domain to another, connecting the two. The time and frequency domains are effective analytical techniques that offer crucial information about signal properties.
The RSA displays the domain information on a graph, which will help identify any problems with the signal. For example, too much noise or distortion will appear on the graph. Real-time analysis is mainly used in research and development settings as it is essential for characterizing signals and testing new equipment.
Real-time Spectrum Analyzer Pros Real-time Spectrum Analyzer Cons Provides a more accurate picture of the signal environment. RSAs do not rely on taking samples of the signal at discrete points in time. Instead, they analyze the signal continuously, providing a 'true' representation of the signal's behavior. Often have reduced frequency resolution. This makes it difficult to identify subtle signal abnormalities. Faster measurement setups and easier identification of signal anomalies. Typically require more time to set up and configure than conventional models. Easily configured to display only the information that is important to you. They offer great flexibility and insight into your signal environment. Real-time analysis is less suited for analyzing very low-frequency signals.There are different aspects you will need to consider when shopping for a spectrum analyzer; from the resolution bandwidth to the phase noise performance and amplitude range. Let's take a look at seven of these main aspects.
The frequency range is an important factor to consider because different spectrum analyzers measure different ranges of frequencies.
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For example, some spectrum analyzers measure audio frequencies (20 Hz ' 20 kHz), while others measure radio frequencies (10 MHz ' 10 GHz). Before buying a spectrum analyzer, it is important to figure out what range of frequencies you need to measure. Once you know this, you can narrow your search to a spectrum analyzer that suits your needs.
The resolution bandwidth determines the minimum width of a signal that a spectrum analyzer can resolve, typically expressed in Hertz (Hz).
A high-resolution bandwidth measurement allows for more accurate readings of narrow signals, while a low-resolution bandwidth measurement is suited for reading broad signals. When choosing a resolution bandwidth, it is vital to consider the application for which you will use the spectrum analyzer.
For example, a high-resolution bandwidth will be necessary if your goal is to measure very narrow signals, such as those generated by RF transistors. If your goal is simply to measure the overall power of a signal, then a lower resolution bandwidth will suffice.
The video bandwidth is the width of the frequency range an analyzer can process. The wider the bandwidth, the more information the analyzer can take in. This is crucial when working with complex signals such as those generated by digital devices.
Digital devices often generate signals with very sharp edges, called transients. A wider video bandwidth allows the spectrum analyzer to take more of these transients into account, leading to more accurate measurements and minimizing signal distortion. If you plan on using your spectrum analyzer to measure digital signals, ensure it has a wide enough video bandwidth to accommodate them.
Many spectrum analyzer models offer different bandwidths, so you can be sure to find one that is perfect for what you require.
The span refers to the range of frequencies an analyzer can measure. A wider span means the analyzer can measure a larger range of frequencies, which is helpful for troubleshooting or measuring signals with a wide frequency range. A good rule of thumb is to choose an analyzer with a span that is at least twice the bandwidth of the signal you want to measure.
The influence span has on the sweep time of a signal is also significant. Sweep time refers to the time it takes for the micron beam to travel once across the sample under test. Span directly affects the signal's bandwidth and the amount of information captured in each sweep.
A wider influence span means more information captured per sweep. When selecting a spectrum analyzer, you should consider the amount of data that needs capturing and the desired sweep time.
The amplitude range is the difference between the largest and smallest signal a spectrum analyzer can measure. A larger amplitude range means an analyzer can measure a broader range of signal strengths and frequencies. This is crucial as it allows for accurate measurements of very weak or powerful signals at different frequencies.
There are two key specifications associated with amplitude measurements: absolute accuracy and relative accuracy.
As with actual measurements made by a spectrum analyzer, there are always some inherent measurement errors associated with amplitude range. By understanding these two key specification types, you can select a spectrum analyzer that will be best suited to your application in terms of precision and accuracy.
The input power is the amount of power a spectrum analyzer can accept without damaging the internal components. However, if the input power is too low, the signal will be too weak to produce accurate results. Conversely, if the input power is too high, it will damage the device. To prevent this, most spectrum analyzers have an input power protection feature that limits the amount of power that can enter the device.
It is important to note that a spectrum analyzer's input power specifications differ from the output power specifications. The output power is the amount of power the device can generate.
When choosing a spectrum analyzer, you want to select one with an appropriate input and output power for the signals you will be measuring.
If you are looking for a quality spectrum analyzer, look no further than Keysight. Our premium refurbished analyzers are the best on the market, available at a fraction of the price. We guarantee you will not have to compromise on performance and accuracy when buying from us.
If you are looking for a device that can measure a wide range of signals with precision and reliability, a Keysight premium refurbished spectrum analyzer is a perfect choice.
In this buying guide, we have covered the key features and uses of spectrum analyzers. This information will help you on your quest to purchase a suitable analyzer.
When shopping for an analyzer, consider factors such as phase noise, amplitude range, and input power. It is also important to keep in mind your intended use. Are you an engineer needing a spectrum analyzer to run tests on electronic equipment? Or a hobbyist wanting to check the quality of an audio signal?
Our team will help you find a premium refurbished spectrum analyzer that is right for you.
We are the only company that can provide calibration of your premium used spectrum analyzer. That means you can get straight to work using a reliable and accurate device of the highest standard.
We offer a fast two-week delivery time* for selected spectrum analyzers, which is 6-19 weeks faster than other resellers.
We provide an upgradable warranty, giving you peace of mind knowing you are getting a quality product backed by a trusted manufacturer. Call 1-800-829- and ask to speak to tech support today.
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A spectrum analyzer is a device that allows you to see the frequency components of a signal. It measures each component's strength and the signal's total power.
There are a few different ways to use a spectrum analyzer. The most common is to connect it to an oscilloscope, allowing you to see the signal's shape.
You use a spectrum analyzer to troubleshoot electronic equipment and design and test new equipment.
A spectrum analyzer's frequency range varies from approximately 3 Hz to 50 GHz.
Time domain signals depend on time, whereas the frequency domain rely on frequency. Both domains are related to one another. These domains are effective analytical techniques that offer crucial information about signal properties.
No, a spectrum analyzer is not an oscilloscope. An oscilloscope is a device that allows you to see the shape of a signal. A spectrum analyzer visualizes and analyzes the spectral content of signals.
Yes, a spectrum analyzer measures the power of the spectrum of known and unknown signals.
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